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EXPANDERS WITH RESPECT TO HADAMARD SPACES AND RANDOM GRAPHS

MANOR MENDEL AND ASSAF NAOR

Abstract. It is shown that there exists a sequence of 3-regular graphs {G_{n}}_{n=1}^{∞} and a Hadamard space X such that {G_{n}}_{n=1}^{∞} forms an expander sequence with respect to X, yet random regular graphs are not expanders with respect to X. This answers a question of [NS11]. {G_{n}}_{n=1}^{∞} are also shown to be expanders with respect to random regular graphs, yielding a deterministic sublinear time constant factor approximation algorithm for computing the average squared distance in subsets of a random graph. The proof uses the Euclidean cone over a random graph, an auxiliary continuous geometric object that allows for the implementation of martingale methods.

Contents

1. Introduction 2 2. Sublinear average distance approximation algorithms 8 2.1. A question of J. Kleinberg 11 3. Preliminaries 12 3.1. Graph theoretical definitions 12 3.2. Bi-Lipschitz embeddings 14 3.3. Nonlinear absolute spectral gaps 14 3.4. Graph products, edge completion, and Cesàro averages 15 3.4.1. A zigzag iteration 17 3.5. CAT (0) and CAT (1) spaces 18 3.5.1. Nonlinear spectral calculus for CAT (0) spaces 20 3.6. The Euclidean cone 20 3.7. CAT (0) cones 22 3.8. Auxiliary families of graphs 23 3.9. On the structure of cones over random graphs 25 3.10. Unions of cones 26 4. Proof of Theorem 1.1 and Theorem 1.2 27 4.1. A stronger theorem 27 4.2. Proof of Theorem 4.1 28 5. On the Lipschitz structure of Euclidean cones 33 5.1. The Euclidean cone over L_{1} 35 5.2. Snowflakes of cones 39 5.3. Poincaré inequalities with respect to unions of cones 42 6. Embedding (1 + δ)-sparse graphs in L_{1} 45 7. Geometric properties of random regular graphs 55 7.1. Proof of Lemma 3.12 59 7.2. Proof of Proposition 3.15 66 8. Are two stochastically independent random graphs expanders with

respect to each other? 68 References 71

1. Introduction

Throughout this paper all graphs are unweighted, non-oriented, and finite, and they are allowed to have parallel edges and self-loops, unless stated otherwise. Given a graph G, we denote its vertices by V_{G} and its edges by E_{G}. If G is connected then we denote the shortest-path metric that it induces on V_{G} by d_{G}.

Fix d ∈ ℕ and let {G_{n}}_{n=1}^{∞} be a sequence of d-regular graphs such that lim_{n→∞} |V_{G_{n}}| = ∞. Then {G_{n}}_{n=1}^{∞} is an expander sequence (see e.g. [HLW06]) if and only if for every sequence of Hilbert space valued functions {f_{n} : V_{n} → ℓ_{2}}_{n=1}^{∞} we have

\frac{1}{|V_{G_{n}}|^{2}} ∑_{(x,y)∈V_{Gn}×V_{Gn}} ∥f_{n}(x) − f_{n}(y)∥_{2}^{2}

≍ \frac{1}{|E_{G_{n}}|} ∑_{\{u,v\}∈E_{Gn}} ∥f_{n}(u) − f_{n}(v)∥_{2}^{2}. (1)

Here and in what follows, when we write A ≍ B we mean that there exist two universal constants c, C ∈ (0, ∞) such that cA ⩽ B ⩽ CB. Also, in what follows the notations A ≲ B and B ≳ A mean that A ⩽ KB for some universal constant K ∈ (0, ∞). If we need to allow K to depend on parameters, we indicate this by subscripts, thus e.g. A ≲_{α,β} B means that A ⩽ K(α, β)B for some K(α, β) ∈ (0, ∞) which is allowed to depend only on the parameters α and β.

The asymptotic identity (1) says that whenever one assigns a vector to each vertex of G_{n}, the average squared distance between these vectors can be estimated up to universal constant factors by averaging those squared distances that correspond to edges of G_{n}, an average of only d|V_{G_{n}}| numbers rather than the full |V_{G_{n}}|^{2} pairwise distances. This geometric characterization of expanders as “universal average Euclidean distance approximators” (following terminology of [BGS07]) is easy to prove (its proof will also be explained in the ensuing discussion).

It is natural to investigate the possible validity of (1) when the Hilbertian metric is replaced other metrics. (1) comprises of two asymptotic inequalities, one of which holds true in any metric space: if G is a d-regular graph and (X, d_{X}) is a metric space then for every f : V_{G} → X we have

\frac{1}{|E_{G}|} ∑_{\{u,v\}∈E_{G}} d_{X}(f(u), f(v))^{2} ⩽ \frac{4}{|V_{G}|^{2}} ∑_{(x,y)∈V_{G}×V_{G}} d_{X}(f(x), f(y))^{2}. (2)

Indeed, by the triangle inequality and the convexity of t ↦ t^{2},

d_{X}(f(u), f(v))^{2} ⩽ 2d_{X}(f(u), f(w))^{2} + 2d_{X}(f(w), f(v))^{2}  (3)

for every u, v, w ∈ V_{G}. Since G is d-regular, the bound (2) follows by averaging (3) over the set {(u, v, w) ∈ V_{G} × V_{G} × V_{G} : {u, v} ∈ E_{G}}.

The nontrivial content of (1) is therefore the fact that the left hand side of (1) can be bounded by a multiple (independent of n and f) of the right hand side of (1). Thus, given a regular graph G and a metric space (X, d_{X}), let γ(G, d_{X}^{2}) be the infimum over those γ ∈ (0, ∞] such that for every f : V_{G} → X,

\frac{1}{|V_{G}|^{2}} ∑_{(u,v)∈V_{G}×V_{G}} d_{X}(f(u), f(v))^{2} ⩽ \frac{γ}{|E_{G}|} ∑_{\{u,v\}∈E_{G}} d_{X}(f(u), f(v))^{2}. (4)

If X = ℝ with d_{ℝ}(s, t) =^{def} |s−t| then by expanding the squares in (4) one checks that

γ(G, d_{ℝ}^{2}) = \frac{1}{1 − λ_{2}(G)},

where λ_{2}(G) denotes the second largest eigenvalue of the normalized adjacency matrix of the graph G. Despite the fact that there is no actual spectrum present in this geometric context, we think of γ(G, d_{X}^{2}) as the reciprocal of the spectral gap of G with respect to (X, d_{X}). The value of γ(G, d_{X}^{2}) is sensitive to the choice of metric space (X, d_{X}) and it can be very different from the reciprocal of the spectral gap of G. We refer to [MN14] for more information on nonlinear spectral gaps.

Given d ∈ ℕ, a sequence of d-regular graphs {G_{n}}_{n=1}^{∞} is said to be an expander sequence with respect to a metric space (X, d_{X}) if lim_{n→∞} |V_{G_{n}}| = ∞ and sup_{n∈ℕ} γ(G_{n}, d_{X}^{2}) < ∞. Note that by Cheeger’s inequality [Che70, AM85] for every graph G we have

|X| ⩾ 2 ⟹ γ(G, d_{X}^{2}) ≳ \frac{1}{\sqrt{1 − λ_{2}(G)}}.

Hence, unless X is a singleton, if {G_{n}}_{n=1}^{∞} is an expander sequence with respect to (X, d_{X}) then sup_{n∈ℕ} λ_{2}(G_{n}) < 1. This means that for every nontrivial metric space (X, d_{X}), being an expander sequence with respect to (X, d_{X}) is a stronger requirement than being an expander sequence in the classical sense.

Nonlinear spectral gaps first arose in the context of bi-Lipschitz embeddings; notable examples include the works of Enflo [Enf76], Gromov [Gro83], Bourgain, Milman and Wolfson [BMW86], Pisier [Pis86], Linial, London and Rabinovich [LLR95], and Matoušek [Mat97] (examples of more recent applications of nonlinear spectral gaps to bi-Lipschitz embeddings appear in [BLMN05, KN06]). Gromov [Gro03] studied nonlinear spectral gaps in the context of coarse embeddings and the Novikov conjecture, a direction that has been pursued in the works of Ozawa [Oza04], Kasparov and Yu [KY06], V. Lafforgue [Laf08, Laf09, Laf10], Pisier [Pis10], and ourselves [MN10, MN14]. Nonlinear spectral gaps also arise in fixed point theory for group actions; see the works of Wang [Wan98, Wan00], Gromov [Gro03], Izeki and Nayatani [IN05], Pansu [Pan09], Naor and Silberman [NS11], and Izeki, Kondo and Nayatani [IKN12]. We refer to the works of Gromov [Gro01] and Pichot [Pic08] for additional geometric applications of nonlinear spectral gaps. In Section 2 we discuss the relevance of nonlinear spectral gaps to approximation algorithms, based on ideas of Indyk [Ind99] and Barhum, Goldreich and Shraibman [BGS07].

Answering a question of Kasparov and Yu [KY06], V. Lafforgue proved [Laf08] that there exists a sequence of bounded degree graphs that are expanders with respect to every uniformly convex normed space; such graph sequences are called super-expanders. In [MN14] we found a different construction of super-expanders; here we show that our method can be applied to situations in which it seems difficult to use Lafforgue’s (algebraic) approach.

It is a challenging question to characterize those metric spaces with respect to which there exist expander sequences. Random regular graphs are with high probability expanders in the classical sense (i.e., with respect to ℝ), but we are far from understanding those metric spaces with respect to which random regular graphs are expanders. One of the consequences of the results obtained here is that there exists a metric space (X, d_{X}) with respect to which there exists an expander sequence, yet almost surely random regular graphs are not expanders with respect to (X, d_{X}). No such example was previously known.

Let 𝔾_{n} be the set of all graphs on the vertex set {1, …, n}. The subset of 𝔾_{n} consisting of all the connected graphs is denoted 𝔾_{n}^{con}. Given an integer d ⩾ 3, let G_{n,d} be the probability measure on 𝔾_{n} which is uniform over all those graphs in 𝔾_{n} that are d-regular and have no self-loops and no parallel edges. By [Wor81], lim_{n→∞} G_{n,d}(𝔾_{n}^{con}) = 1.

A Hadamard space (also known as a complete CAT (0) space) is a complete metric space (X, d_{X}) with the property that for every x, y ∈ X there exists a point w ∈ X such that for every z ∈ X we have

d_{X}(z, w)^{2} + \frac{1}{4}d_{X}(x, y)^{2} ⩽ \frac{1}{2}d_{X}(z, x)^{2} + \frac{1}{2}d_{X}(z, y)^{2}.  (5)

See the books [Jos97, BH99] and the survey [Stu03] for an extensive account of Hadamard spaces. One of the main questions left open in [NS11] (specifically, see page 1547 of [NS11]) is whether or not it is true that if (X, d_{X}) is a Hadamard space that admits at least one expander sequence then every classical expander sequence is also an expander sequence with respect to (X, d_{X}). Theorem 1.1 below, which is the first of our two main theorems, answers this question.

Theorem 1.1. There exist a Hadamard space (X, d_{X}) and a sequence of 3-regular graphs {G_{n}}_{n=1}^{∞} with lim_{n→∞} |V_{G_{n}}| = ∞ such that

sup_{n∈ℕ} γ(G_{n}, d_{X}^{2}) < ∞,  (6)

yet there exists c ∈ (0, ∞) such that for every d ∈ ℕ,

lim_{n→∞} G_{n,d} ({H ∈ 𝔾_{n} : γ(H, d_{X}^{2}) ⩾ c(log_{d} n)^{2}}) = 1.  (7)

Theorem 1.1 answers the above mentioned question from [NS11]. Indeed, by (6) the Hadamard space (X, d_{X}) admits some expander sequence. Random regular graphs are asymptotically almost surely classical expanders [Bol88], i.e., there exists C ∈ (0, ∞) such that for every integer d ⩾ 3,

lim_{n→∞} G_{n,d} ({H ∈ 𝔾_{n} : γ(H, d_{ℝ}^{2}) ⩽ C}) = 1.  (8)

Consequently, it follows from (7) and (8) that not all classical expander sequences are expanders with respect to (X, d_{X}).

Theorem 1.1 yields the first known example of a metric space (X, d_{X}) with respect to which random regular graphs are asymptotically almost surely not expanders, yet there does exist a special graph sequence {G_{n}}_{n=1}^{∞} that is an expander sequence with respect to (X, d_{X}). Observe that {G_{n}}_{n=1}^{∞} is a fortiori a classical expander sequence.

The graphs {G_{n}}_{n=1}^{∞} of Theorem 1.1 have desirable properties which no other expander sequence is known to satisfy. Specifically, {G_{n}}_{n=1}^{∞} are expanders with respect to random regular graphs. This is made precise in the following theorem.

Theorem 1.2. There exists a universal constant Γ ∈ (0, ∞) and a sequence of 3-regular graphs {G_{n}}_{n=1}^{∞} with

lim_{n→∞} |V_{G_{n}}| = ∞  and  sup_{n∈ℕ} \frac{|V_{G_{n+1}}|}{|V_{G_{n}}|} < ∞,  (9)

such that for every integer d ⩾ 3 we have

lim_{m→∞} G_{m,d} ({H ∈ 𝔾_{m}^{con} : sup_{n∈ℕ} γ(G_{n}, d_{H}^{2}) < Γ}) = 1. (10)

In (10) we restrict to H ∈ 𝔾_{m}^{con} because the metric d_{H} is defined only when H is connected. Explicit bounds on the rates of convergence in (7) and (10) are given Section 4.1.

Take G ∈ {G_{n}}_{n=1}^{∞} and write V_{G} = {1, …, k} for some k ∈ ℕ. Theorem 1.2 asserts that almost surely as m → ∞, if H is a uniformly random m-vertex d-regular graph then for every v_{1}, …, v_{k} ∈ V_{H} the average of d_{H}(v_{i}, v_{j})^{2} over all i, j ∈ {1, …, k} is at most a constant multiple of the average of d_{H}(v_{i}, v_{j})^{2} over those i, j ∈ {1, …, k} that are joined by an edge of G. Note that the latter average is over 3k/2 ≍ k numbers while the former average is over \binom{k}{2} ≍ k^{2} numbers.

Thus G is an especially constructed fixed graph that serves as a “sparse template” for computing the average squared distance between any k vertices in a random regular graph. This yields sublinear (deterministic) time approximate computation of average distances in random graphs: our input size is Ω(k^{2}), namely all the pairwise distances, while using G we estimate \frac{1}{k^{2}} ∑_{i=1}^{k} ∑_{j=1}^{k} d_{H}(x_{i}, x_{j})^{2} by making only O(k) distance queries. See Section 2 for more on this topic.

Once the graph G is given to us, the above statement about the average squared shortest-path distance of any k vertices of the random graph H involves elementary combinatorics and probability. Nevertheless, our proof of this statement uses methods from analysis and geometry that are interesting in their own right.

Specifically, in [MN14] we introduced an iterative approach to the construction of super-expanders, building on the zigzag iteration of Reingold, Vadhan and Wigderson [RVW02]. This approach uses estimates on martingales in uniformly convex Banach spaces. In [MN13] we extended the estimates that were needed for the construction of super-expanders (namely nonlinear spectral calculus inequalities; see Remark 3.3 below) to Hadamard spaces using an appropriate notion of nonlinear martingale. In order to apply these methods in the present setting, we consider the one-dimensional simplicial complex obtained by including all the edges of H as unit intervals. We then work with the Euclidean cone over this one-dimensional simplicial complex, which is an auxiliary two-dimensional (random) continuous object on which martingale methods can be applied. The definition of the Euclidean cone over a metric space will be recalled in Section 3.6 below. We prove that if H is sampled from G_{n,d} then with high probability its Euclidean cone is a (non-disjoint) union of two sets A_{1}, A_{2} such that A_{1} admits a bi-Lipschitz embedding into L_{1} and A_{2} admits a bi-Lipschitz embedding into a Hadamard space. We then treat A_{1} directly using Matoušek’s extrapolation lemma for Poincaré inequalities [Mat97], and we treat A_{2} using the methods of [MN14, MN13].

The implementation of the above strategy is not straightforward, relying on a variety of geometric and analytic tools; a more detailed overview of our proof of Theorem 1.2 appears in Section 3.10 and Section 4.2. At this juncture we only wish to stress that our proof of Theorem 1.2 introduces a way to reason about random graphs that is potentially useful in other contexts: we consider such a graph as being embedded in a larger auxiliary continuous geometric object that allows for the use of analytic methods, even though the statement being proved involves only the vertices of the original graph.

Previous work. Nonlinear spectral gaps have been studied in the literature from several points of view, leading to some notational inconsistencies. Here we use the notation that arises naturally from bi-Lipschitz embedding theory. Pichot [Pic08] denotes the reciprocal of γ(G, d_{X}^{2}) by λ_{1}(G, X). In reference to Gromov’s original definition [Gro01, Gro03], Pansu [Pan09] and Kondo [Kon12] denote the same quantity by λ^{Gro}(G, X) and λ_{1}^{Gro}(G, X), respectively. When X is a Hadamard space, a closely related quantity, known today as Wang’s invariant, was introduced by Wang [Wan98, Wan00]; Wang’s invariant is always within a factor of 2 of the reciprocal of γ(G, d_{X}^{2}). For Hadamard spaces, Izeki and Nayatani [IN05] introduced an invariant that can be used to control the ratio between Wang’s invariant for a graph G and the classical spectral gap of G.

We were motivated to revisit the question of [NS11] that Theorem 1.1 answers by the recent work of Kondo [Kon12]. Kondo’s goal in [Kon12] was to construct a Hadamard space for which the Izeki-Nayatani invariant is trivial. He succeeded to do so by using the Euclidean cone over certain expander graphs. In particular he obtained a Hadamard space that contains bi-Lipschitzly copies of some (classical) expanders. Gromov considered the same construction in [Gro01, Gro03] for a different but related purpose. The main point of Theorem 1.1 is to prove that the space X admits a sequence of expanders; we achieve this by modifying the Gromov-Kondo construction so that it will be compatible with the method to construct nonlinear expanders of [MN14]. The fact that our graphs are expanders with respect to random graphs requires additional work, relying on a structural result for Euclidean cones over random graphs that is presented in Section 3.9.

Roadmap. In Section 2 we describe an algorithmic implication of Theorem 1.2. Because the proofs of Theorem 1.1 and Theorem 1.2 use ingredients from several fields, Section 3 is devoted to a detailed explanation of the background and main tools that will be used in the proof of Theorem 1.1 and Theorem 1.2. The proofs themselves are given in Section 4, a section that is self-contained modulo some (quite substantial) ingredients that are presented in Section 3 and whose proof appears in later sections. The high-level structure of the argument is best discerned from reading Section 4, since it uses as a “black box” some conceptual ingredients whose proof is quite lengthy. Section 3.6 investigates the Lipschitz structure of Euclidean cones, proving in particular that the Euclidean cone over L_{1} admits a bi-Lipschitz embedding into L_{1}. Section 6 is devoted to showing that sufficiently sparse graphs admit a bi-Lipschitz embedding into L_{1}. Section 7 deals with random regular graphs, proving in particular a crucial structure theorem (that holds true with high probability) for the Euclidean cone over a random graph. In Section 8 we present a partial result towards an open question that was posed by J. Kleinberg. For the formulation of Kleinberg’s question itself see Section 2.1.

2. Sublinear average distance approximation algorithms

Suppose that (X, d_{X}) is a metric space and we have oracle access to pairwise distances in X. Given x_{1}, …, x_{n} ∈ X write

A =^{def} \frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, x_{j})^{2}.  (11)

One can compute A exactly with n^{2} distance queries. But, we wish to estimate A in sublinear time, i.e., with only o(n^{2}) distance queries.

Indyk [Ind99] proved that it is possible to approximate A up to a factor of 1 + ε by querying the distances between O(n/ε^{7/2}) pairs of points chosen uniformly at random. Barhum, Goldreich and Shraibman [BGS07] improved the required number of uniformly random pairs of points to O(n/ε^{2}), which is asymptotically tight [BGS07].

The above simple randomized sampling algorithm shows that for every x_{1}, …, x_{n} ∈ X one can find O(n) pairs of points from {x_{1}, …, x_{n}} whose average distance is within O(1) of A, but these pairs depend on the initial point set {x_{1}, …, x_{n}} ⊆ X. Following [BGS07], for D ∈ [1, ∞) we say that a graph G = ({1, …, n}, E) is a D-universal approximator with respect to (X, d_{X}^{2}) if there exists (a scaling factor) s ∈ (0, ∞) such that for every x_{1}, …, x_{n} ∈ X we have

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, x_{j})^{2} ⩽ \frac{s}{|E|} ∑_{\{i,j\}∈E} d_{X}(x_{i}, x_{j})^{2} ⩽ \frac{D}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, x_{j})^{2}.

In [BGS07] it is shown that there exists c ∈ (0, ∞) such that if G = ({1, …, n}, E) is a D-universal approximator with respect to (X, d_{X}^{2}) for every metric space (X, d_{X}) then

|E| ≳ \frac{n^{1+c/\sqrt{D}}}{\sqrt{D}} .

It was also shown in [BGS07] that there exists C ∈ (0, ∞) such that for every D ∈ [1, ∞) and n ∈ ℕ there exists a graph G = ({1, …, n}, E) that is a D-universal approximator with respect to (X, d_{X}^{2}) for every metric space (X, d_{X}), and such that

|E| ⩽ \sqrt{D} · n^{1+C/\sqrt{D}}.

We note that [BGS07] deals with the analogous question for universal approximators when the quantity A in (11) is defined with the distances raised to power 1 rather than being squared. However, the arguments of [BGS07] easily extend mutatis mutandis to yield the above stated results (and, in fact, to analogous statements when A is defined in terms of distances raised to power p for any p ⩾ 1).

We thus have a satisfactory understanding of the size of universal approximators with respect to all metric spaces. But, for special metric spaces it is possible to obtain better tradeoffs. Indeed, in [BGS07] it is observed that an expander graph is a linear size O(1)-universal approximator with respect to Hilbert space; this is nothing more than an interpretation of (1), though by being more careful, and using Ramanujan graphs [LPS88, Mar88] of appropriate degree, it is shown in [BGS07] how to obtain a 1 + ε approximation for every ε ∈ (0, 1).

Due to (2) and (4), for every graph G and every metric space (X, d_{X}), if we set D = 4γ(G, d_{X}^{2}) then G is D-universal approximator with respect to (X, d_{X}^{2}). Hence, the super-expanders of [Laf09] and [MN14] are O_{X}(1)-universal approximators with respect to (X, ∥·∥_{X}^{2}) for every uniformly convex Banach space (X, ∥ · ∥_{X}) (by O_{X}(1) we mean that the approximation factor depends only on X, in fact, it depends only on the modulus of uniform convexity of X).

Theorem 1.2 yields the only known construction of bounded degree O(1)-universal approximators with respect to random regular graphs; this is the content of Theorem 2.1 below. Graphs sampled from G_{n,d} occur in various application areas, e.g. in networking, where they serve as models for peer-to-peer networks [MS05]. Therefore, a data structure that can compute quickly the average squared distance of a given subset of a random regular graph is of theoretical interest. However, the potential practicality of our data structure is questionable because the approximation guarantee is a large universal constant; we made no attempt to improve this aspect of the construction.

Theorem 2.1. There exists D ∈ [1, ∞) and for every n ∈ ℕ there exists a graph U_{n} = ({1, …, n}, E_{n}) with |E_{n}| = O(n) such that for every two integers m, d ⩾ 3, if H is sampled from the restriction of G_{m,d} to 𝔾_{m}^{con} then with probability that tends to 1 as m → ∞ the graphs {U_{n}}_{n=1}^{∞} are D-universal approximators with respect to (V_{H}, d_{H}^{2}).

Proof. Let {G_{k}}_{k=1}^{∞} be the graphs from Theorem 1.2. Fixing n ∈ ℕ, it follows from (9) that there exists k ∈ ℕ such that n ⩽ |V_{G_{k}}| ⩽ Mn, where M ∈ ℕ is a universal constant. Partition V_{G_{k}} into n disjoint sets A_{1}, …, A_{n} ⊆ V_{G_{k}} satisfying

∀ i ∈ \{1, …, n\},  |A_{i}| ∈ \{⌊\frac{|V_{G_{k}}|}{n}⌋ , ⌊\frac{|V_{G_{k}}|}{n}⌋ + 1\} . (12)

Define the edge multi-set E_{n} of U_{n} by letting the number of edges joining i ∈ {1, …, n} and j ∈ {1, …, n} be equal to the total number of edges joining A_{i} and A_{j} in G_{k}. Then |E_{n}| ⩽ |E_{G_{k}}| = \frac{3}{2}|V_{G_{k}}| ⩽ \frac{3}{2}Mn.

Suppose that H ∈ 𝔾_{m} is connected and

sup_{k∈ℕ} γ (G_{k}, d_{H}^{2}) < Γ,  (13)

where Γ is the constant from Theorem 1.2. It follows from (10) that if H ∈ 𝔾_{m} is sampled from G_{m,d} then the above asumptions hold true with probability that tends to 1 as m → ∞.

Fix x_{1}, …, x_{n} ∈ V_{H} and define f : V_{G_{k}} → V_{H} by setting f(u) = x_{i} for u ∈ A_{i}. By the definition of γ(G_{k}, d_{H}^{2}) combined with (13) and (2),

\frac{1}{|V_{G_{k}}|^{2}} ∑_{(u,v)∈V_{G_{k}} ×V_{G_{k}}} d_{H}(f(u), f(v))^{2} ⩽ \frac{Γ}{|E_{G_{k}}|} ∑_{\{u,v\}∈E_{G_{k}}} d_{H}(f(u), f(v))^{2}

⩽ \frac{4Γ}{|V_{G_{k}}|^{2}} ∑_{(u,v)∈V_{G_{k}} ×V_{G_{k}}} d_{H}(f(u), f(v))^{2}. (14)

Since

∑_{(u,v)∈V_{G_{k}} ×V_{G_{k}}} d_{H}(f(u), f(v))^{2} = ∑_{i=1}^{n} ∑_{j=1}^{n} |A_{i}| · |A_{j}| · d_{H}(x_{i}, x_{j})^{2},

it follows from (12) that

\frac{n^{2}⌊|V_{G_{k}}|/n⌋^{2}}{|V_{G_{k}}|^{2}} ⩽ \frac{\frac{1}{|V_{G_{k}} |^{2}} ∑_{(u,v)∈V_{G_{k}} ×V_{G_{k}}} d_{H}(f(u), f(v))^{2}}{\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(x_{i}, x_{j})^{2}}

⩽ \frac{n^{2} (⌊|V_{G_{k}}|/n⌋ + 1)^{2}}{|V_{G_{k}}|^{2}} .  (15)

Also, by the definition of E_{n} and f we have

∑_{{u,v}∈E_{G_{k}}} d_{H}(f(u), f(v))^{2} = ∑_{{i,j}∈E_{n}} d_{H}(x_{i}, x_{j})^{2}.  (16)

By substituting (15) and (16) into (14) we conclude that

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(x_{i}, x_{j})^{2} ⩽ \frac{Γ|V_{G_{k}}|^{2}|E_{n}|}{n^{2}⌊|V_{G_{k}}|/n⌋^{2}|E_{G_{k}}|} · \frac{1}{|E_{n}|} ∑_{\{i,j\}∈E_{n}} d_{H}(x_{i}, x_{j})^{2}

⩽ \frac{4Γ (1 + \frac{1}{⌊|V_{G_{k}} |/n⌋})^{2}}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(x_{i}, x_{j})^{2}

⩽ \frac{16Γ}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(x_{i}, x_{j})^{2}.

This means that U_{n} is a D-universal approximator with respect to (V_{H}, d_{H}^{2}), where D = 16Γ. □

Remark 2.2. A straightforward inspection of our proof of Theorem 1.2 reveals that for every n ∈ ℕ the universal approximator U_{n} of Theorem 2.1 can be constructed in deterministic O(n) time.

Remark 2.3. Let (X, d_{X}) be a metric space and p ∈ [1, ∞). As noted above, one can study universal approximators with respect to (X, d_{X}^{p}), i.e., given x_{1}, …, x_{n} ∈ X the goal is approximate computation of the quantity \frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, x_{j})^{p} with o(n^{2}) distance queries. The references [Ind99, BGS07] deal with p = 1, but as we mentioned earlier, they extend painlessly to general p ⩾ 1. Here we have only dealt with the case p = 2, but we speculate that our argument can be modified to yield bounded degree universal approximators with respect to (H, d_{H}^{p}) for any p ∈ (1, ∞), where H is a random d-regular graph. We did not, however, attempt to carry out our proof when p ≠ 2. We also speculate that the case p = 1 requires more substantial new ideas, as the type of martingale arguments that we use typically fail at the endpoint p = 1.

2.1. A question of J. Kleinberg. In connection with Theorem 1.2 and the algorithmic context described in Section 2, Jon Kleinberg asked us whether or not two stochastically independent random 3-regular graphs are asymptotically almost surely expanders with respect to each other. Formally, we have the following open question.

Question 2.4 (Jon Kleinberg). Does there exist a universal constant K ∈ (0, ∞) such that

lim_{min{m,n}→∞} G_{m,3} × G_{n,3} [{(G, H) ∈ 𝔾_{m} × 𝔾_{n}^{con} : γ (G, d_{H}^{2})} ⩽ K] = 1.

While a positive solution of Question 2.4 does not formally imply that there exist graphs {G_{n}}_{n=1}^{∞} as in Theorem 1.2, such a statement would be a step towards a probabilistic construction of such graphs. At present we do not have methods to argue about the nonlinear spectral gap of random graphs. In particular, it is a major open question whether or not random 3-regular graphs are super-expanders with positive probability. Thus, all the known constructions in the context of nonlinear spectral gaps are deterministic, with the only two methods that are currently available being Lafforgue’s algebraic approach [Laf08] and our iterative approach [MN14]. It seems, however, that the problem of obtaining a probabilistic proof of the existence of expanders with respect to random graphs is more tractable, and for this reason we believe that Question 2.4 is a promising research direction. In Section 8 we describe a partial result towards a positive solution of Question 2.4 that we obtained through discussions with Uriel Feige; we thank him for allowing us to include his insights here.

3. Preliminaries

In this section we set notation and terminology that will be used throughout the ensuing discussion. We also present the tools and main steps that will be used in the proofs of Theorem 1.1 and Theorem 1.2.

3.1. Graph theoretical definitions. We start by defining some basic concepts in graph theory. Most of what we describe here is standard or self-evident terminology, and in fact some of it was used in the introduction without being defined explicitly.

Recall that graphs can have parallel edges and self-loops unless stated otherwise. Thus, when discussing a graph G it will always be understood that E_{G} is a multi-subset of unordered pairs of vertices, i.e., for every u, v ∈ V_{G} the unordered pair {u, v} is allowed to appear in E_{G} multiple times. For (u, v) ∈ V_{G} ×V_{G}, denote by E_{G}(u, v) the number of times that {u, v} appears in E_{G}. The graph G is therefore determined by the integer matrix (E_{G}(u, v))_{(u,v)∈V_{G}×V_{G}}. A graph is called simple if it contains no self-loops and no parallel edges, which is equivalent to requiring that E_{G}(u, u) = 0 for every u ∈ V_{G} and E_{G}(u, v) ∈ {0, 1} for every u, v ∈ V_{G}. Given S, T ⊆ V_{G}, we denote by E_{G}(S, T) the multi-set of all edges in E_{G} that join a vertex in S and a vertex in T (hence for (u, v) ∈ V_{G} × V_{G} we have E_{G}(u, v) = |E_{G}({u}, {v})|). When S = T we use the simpler notation E_{G}(S) = E_{G}(S, S).

The degree of a vertex u ∈ V_{G} is deg_{G}(u) = ∑_{v∈V_{G}} E_{G}(u, v). Under this convention each self-loop contributes 1 to the degree of a vertex. For d ∈ ℕ, a graph G is d-regular if deg_{G}(u) = d for every u ∈ V_{G}.

For a graph G and a symmetric function K : V_{G} ×V_{G} → ℝ satisfying K(u, u) = 0 for every u ∈ V_{G}, when we write the sum ∑_{{u,v}∈E_{G}} K(u, v) we mean that each edge is counted once, i.e,

∑_{\{u,v\}∈E_{G}} K(u, v) =^{def} \frac{1}{2} ∑_{\begin{matrix}(u,v)∈V_{G}×V_{G} \\ \{u,v\}∈E_{G} \end{matrix}} K(u, v)

= \frac{1}{2} ∑_{(u,v)∈V_{G}×V_{G}} E_{G}(u, v)K(u, v).

If G is a connected graph then the diameter of the metric space (V_{G}, d_{G}) is denoted diam(G). The one-dimensional simplicial complex induced by a graph G is denoted Σ(G). Thus Σ(G) is obtained from G by including the edges of G as unit intervals. The geodesic distance on Σ(G) is denoted d_{Σ(G)}. Then

diam(G) ⩽ diam(Σ(G)) ⩽ diam(G) + 1.  (17)

Given a graph G, a subset C ⊆ V_{G} is called a cycle of G if it is possible to write C = {x_{1}, …, x_{k}}, where the vertices x_{1}, …, x_{k} ∈ V are distinct and

{x_{1}, x_{2}}, {x_{2}, x_{3}}, …, {x_{k−1}, x_{k}}, {x_{k}, x_{1}} ∈ E_{G}.  (18)

Thus a self-loop is a cycle of length 1 and a parallel edge induces a cycle of length 2. By definition, |E_{G}(C)| ⩾ |C| for every cycle C ⊆ V_{G}. A cycle C ⊆ V_{G} is said to be an induced cycle if |E_{G}(C)| = |C|, i.e., E_{G}(C) consists only of the edges listed in (18). Note that the smallest cycle in G is necessarily induced. The girth of a graph G, denoted girth(G), is the size of the smallest cycle in G. If a connected graph G does not contain a cycle, i.e., G is a tree, then we shall use the convention girth(G) = 2 diam(G).

The normalized adjacency matrix of a d-regular graph G, denoted A_{G}, is the |V_{G}| by |V_{G}| symmetric stochastic matrix whose entry at (u, v) ∈ V_{G} × V_{G} is equal to E_{G}(u, v)/d. The decreasing rearrangement of the eigenvalues of A_{G} is denoted

1 = λ_{1}(G) ⩾ λ_{2}(G) ⩾ … ⩾ λ_{|V_{G}|}(G).

3.2. Bi-Lipschitz embeddings. A metric space (U, d_{U}) is said to admit a bi-Lipschitz embedding with distortion at most D ∈ [1, ∞) into a metric space (V, d_{V}) if there exists (a scaling factor) s ∈ (0, ∞) and f : U → V that satisfies

∀ x, y ∈ U,  sd_{U}(x, y) ⩽ d_{V} (f(x), f(y)) ⩽ Dsd_{U}(x, y).  (19)

The infimum over those D ∈ (0, ∞) for which (U, d_{U}) admits a bi-Lipschitz embedding with distortion at most D into (V, d_{V}) is denoted c_{(V,d_{V} )}(U, d_{U}), or simply c_{V} (U) if the respective metrics are clear from the context (if no such D ∈ (0, ∞) exists then we set c_{V} (U) = ∞). For p ∈ [1, ∞] we use the simpler notation c_{p}(U) = c_{L_{p}}(U). The quantity c_{2}(U) is known as the Euclidean distortion of U and the quantity c_{1}(U) is known as the L_{1} distortion of U.

If G is a connected simple graph with |V_{G}| = n then c_{p}(Σ(G)) ≲ n for every p ∈ [1, ∞]. Indeed, write V_{G} = {1, …, n} and let e_{1}, …, e_{n} ∈ ℝ^{n} be the coordinate basis of ℝ^{n}. If {i, j} ∈ E_{G} and x ∈ Σ(G) is a point on the unit interval that corresponds to the edge joining i and j, then map x to (1 − t)e_{i} + te_{j}, where t is the distance between x and i in Σ(G). This trivial embedding can be improved: one has c_{p}(Σ(G)) ≲ log(n + 1). The estimate c_{p}(V_{G}, d_{G}) ≲ log(n + 1) is the classical Bourgain embedding theorem [Bou85], but it is simple to argue that the same bound holds true for embeddings of the one-dimensional simplicial complex of G rather than just its vertices. Using the better bound c_{p}(Σ(G)) ≲ log(n + 1) in what follows can improve the implicit constants in Theorem 1.1 and Theorem 1.2, but since we ignore constant factors here it will suffice to use the trivial bound c_{p}(Σ(G)) ≲ n.

For future use we also recall that an argument from [LLR95] (see also [MN14, Sec. 1.1]) shows that for every connected d-regular graph H and every metric space (Z, d_{Z}),

c_{(Z,d_{Z})} (V_{H}, d_{H}) ≳ \frac{log_{d} n}{\sqrt{γ(H, d_{Z}^{2})}} .  (20)

3.3. Nonlinear absolute spectral gaps. Fix d, n ∈ ℕ and suppose that G is an n-vertex d-regular graph and that (X, d_{X}) is a metric space. Define γ_{+}(G, d_{X}^{2}) to be the infimum over those γ_{+} ∈ (0, ∞] such that for every two mappings f, g : V_{G} → X we have

\frac{1}{n^{2}} ∑_{(u,v)∈V_{G}×V_{G}} d_{X}(f(u), g(v))^{2}

⩽ \frac{γ_{+}}{nd} ∑_{(u,v)∈V_{G}×V_{G}} E_{G}(u, v) · d_{X}(f(u), g(v))^{2}. (21)

Comparison of (21) to (4) reveals that γ(G, d_{X}^{2}) ⩽ γ_{+}(G, d_{X}^{2}). With this notation one has

γ_{+}(G, d_{ℝ}^{2}) = \frac{1}{1 − max \{λ_{2}(G), −λ_{|V_{G}|}(G\}}.

For this reason we think of γ_{+}(G, d_{X}^{2}) as measuring the reciprocal of the nonlinear absolute spectral gap of G with respect to (X, d_{X}).

Despite the fact that Theorem 1.1 and Theorem 1.2 deal with the quantity γ(·, ·), for their proofs it will be very convenient to work with the quantity γ_{+}(·, ·). See [MN14] for more on nonlinear absolute spectral gaps, specifically Section 2.2 of [MN14] for information on the relation between γ(·, ·) and γ_{+}(·, ·).

As in [MN14], it is convenient to also work with nonlinear spectral gaps of symmetric stochastic matrices. Thus, letting M = (m_{ij}) be an n by n symmetric stochastic matrix and (X, d_{X}) be a metric space, define γ(M, d_{X}^{2}) to be the infimum over those γ ∈ (0, ∞] such that for every x_{1}, …, x_{n} ∈ X we have

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, x_{j})^{2} ⩽ \frac{γ}{n} ∑_{i=1}^{n} ∑_{j=1}^{n} m_{ij}d_{X}(x_{i}, x_{j})^{2}.

Also, define γ_{+}(M, d_{X}^{2}) to be the infimum over those γ_{+} ∈ (0, ∞] such that for every x_{1}, …, x_{n}, y_{1}, …, y_{n} ∈ X we have

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, y_{j})^{2} ⩽ \frac{γ}{n} ∑_{i=1}^{n} ∑_{j=1}^{n} m_{ij}d_{X}(x_{i}, y_{j})^{2}.

Under these definitions, one checks that for every regular graph G we have γ(G, d_{X}^{2}) = γ(A_{G}, d_{X}^{2}) and γ_{+}(G, d_{X}^{2}) = γ_{+}(A_{G}, d_{X}^{2}).

3.4. Graph products, edge completion, and Cesàro averages. Fix d_{1}, d_{2} ∈ ℕ. Let G_{1} be a d_{1}-regular graph and let G_{2} be a d_{2}-regular graph. Suppose that |V_{G_{2}}| = d_{1}. Then one can construct a new graph, called the zigzag product of G_{1} and G_{2} and denoted G_{1}\begin{matrix}z \\ ◯ \end{matrix}G_{2}. This construction is due to Reingold, Vadhan and Wigderson [RVW02]. We will not need to recall the definition of G_{1}\begin{matrix}z \\ ◯ \end{matrix}G_{2} here: all that we will use below is that G_{1}\begin{matrix}z \\ ◯ \end{matrix}G_{2} is d_{2}^{2}-regular, |V_{G_{1}\begin{matrix}z \\ ◯ \end{matrix}G_{2}}| = |V_{G_{1}}| · |V_{G_{2}}|, and for every metric space (X, d_{X}) we have

γ_{+}(G_{1}\begin{matrix}z \\ ◯ \end{matrix}G_{2}, d_{X}^{2}) ⩽ γ_{+}(G_{1}, d_{X}^{2}) · γ_{+}(G_{2}, d_{X}^{2})^{2} .  (22)

The inequality (22) is due to [MN14].

Under the same assumptions on the graphs G_{1}, G_{2}, i.e., that G_{1} is d_{1}-regular, G_{2} is d_{2}-regular, and |V_{G_{2}}| = d_{1}, one can also construct a new graph, called the replacement product of G_{1} and G_{2} and denoted G_{1}\begin{matrix}r \\ ◯ \end{matrix}G_{2}. This construction is due to Gromov [Gro83] (see also [RVW02] and [MN14, Sec 8.3]). Again, we will not need to recall the definition of G_{1}\begin{matrix}r \\ ◯ \end{matrix}G_{2} here: all that we will use below is that the graph G_{1}\begin{matrix}r \\ ◯ \end{matrix}G_{2} is (d_{2} + 1)-regular, |V_{G_{1}\begin{matrix}r \\ ◯ \end{matrix}G_{2}}| = |V_{G_{1}}| · |V_{G_{2}}|, and for every metric space (X, d_{X}) we have

γ_{+}(G_{1}\begin{matrix}r \\ ◯ \end{matrix}G_{2}, d_{X}^{2}) ⩽ 3(d_{2} + 1) · γ_{+}(G_{1}, d_{X}^{2}) · γ_{+}(G_{2}, d_{X}^{2})^{2} .  (23)

The inequality (23) is due to [MN14].

Fix d ∈ ℕ and suppose that G is a d-regular graph. For every integer D ⩾ d one can define a new graph called the D-edge completion of G, and denoted 𝒞_{D}(G). See [MN14, Def. 2.8] for the definition of 𝒞_{D}(G). All that we will use below is that 𝒞_{D}(G) is D-regular, V_{𝒞_{D}(G)} = V_{G}, and for every metric space (X, d_{X}) we have

γ_{+}(𝒞_{D}(G), d_{X}^{2}) ⩽ 2γ_{+}(G, d_{X}^{2}) .  (24)

The proof of (24) is contained in [MN14, Lem. 2.9].

We will also work below with Cesàro averages of graphs. Given d, m ∈ ℕ and a d-regular graph G, its mth Cesàro average 𝒜_{m}(G) is a new graph defined by V_{𝒜_{m}(G)} = V_{G} and

∀ (u, v) ∈ V_{G} × V_{G}, E_{𝒜_{m}(G)} (u, v) =^{def} ∑_{t=0}^{m−1} d^{m−1−t} (A_{G}^{t})_{u,v} ,

where we recall that A_{G} is a the normalized adjacency matrix of G. One checks that 𝒜_{m}(G) is md^{m−1}-regular and that the adjacency matrix of 𝒜_{m}(G) is the corresponding Cesàro average of A_{G}, i.e.,

A_{𝒜_{m}(G)} = \frac{1}{m} ∑_{t=0}^{m−1} A_{G}^{t}.

The following lemma will be used (twice) in Section 4.2.

Lemma 3.1. Fix two integers d, n ⩾ 3 and let G be a d-regular graph with |V_{G}| = 2n. Then there exists a 3-regular graph G^{∗} satisfying |V_{G^{∗}}| = 36dn = 18d|V_{G}| such that for every metric space (X, d_{X}),

γ_{+}(G^{∗}, d_{X}^{2}) ≲ d^{4}γ(G, d_{X}^{2}) .  (25)

Proof. By Lemma 2.6 in [MN14], there exists a 4d-regular graph G′ with |V_{G′}| = n such that γ_{+}(G′, d_{X}^{2}) ⩽ 8γ(G, d_{X}^{2}) for every metric space (X, d_{X}). Let C_{4d}^{∘} be the cycle of length 4d in which each vertex has exactly one self-loop (thus C_{4d}^{∘} is a 3-regular graph). Since G′ is a 4d-regular graph, we may form the zigzag product G′′\begin{matrix}z \\ ◯ \end{matrix}C_{4d}^{∘}, which is a 9-regular graph with 4dn vertices. By [MN14, Lem. 2.1] we have γ_{+}(C_{4d}^{∘}, d_{X}^{2}) ⩽ 192d^{2}. It therefore follows from (22) that

γ_{+}(G′′, d_{X}^{2}) ⩽ 8γ(G, d_{X}^{2}) · (192d^{2})^{2} ≲ d^{4}γ(G, d_{X}^{2}).  (26)

Now, let C_{9} be the cycle of length 9 (thus C_{9} is a 2-regular graph with 9 vertices). Define G^{∗} to be the replacement product G^{∗} = G′′\begin{matrix}r \\ ◯ \end{matrix}C_{9}, which is a 3-regular graph with 36dn vertices. By [MN14, Lem. 2.1] we have γ_{+}(C_{9}, d_{X}^{2}) ⩽ 648, so (25) follows from (26) and (23). □

3.4.1. A zigzag iteration. Theorem 3.2 below is a (much simpler) variant of the iterative procedure by which we constructed super-expanders in [MN14], building on the zigzag iteration of Reingold, Vadhan and Wigderson [RVW02].

Theorem 3.2. Fix K ∈ [1, ∞) and two integers n, d ⩾ 3 with n ⩾ d^{3}. Suppose that (X, d_{X}) is a metric space with the property that for every regular graph G we have

γ_{+}(𝒜_{m}(G), d_{X}^{2}) ⩽ K max \{1, \frac{γ_{+}(G, d_{X}^{2})}{m} \} ,  (27)

where

m =^{def} ⌊\frac{log n}{3 log d}⌋ .  (28)

Suppose further that there exists a d-regular graph H with |V_{H}| = n such that

γ_{+}(H, d_{X}^{2}) ⩽ \sqrt{\frac{m}{2K}} .  (29)

Then there exists a sequence of 3-regular graphs {G_{j}}_{j=1}^{∞} satisfying

∀ j ∈ ℕ,  |V_{G_{j}} | = 9d^{2}n^{j},  (30)

and

∀ j ∈ ℕ,  γ_{+}(G_{j}, d_{X}^{2}) ≲ d^{8}Kγ_{+}(H, d_{X}^{2})^{2} .  (31)

Proof. We first claim that for every j ∈ ℕ there exists a d^{2}-regular graph W_{j} such that |V_{W_{j}}| = n^{j} and

γ_{+}(W_{j}, d_{X}^{2}) ⩽ 2Kγ_{+}(H, d_{X}^{2})^{2} .  (32)

The proof of this statement is by induction on j. Define W_{1} to be the d^{2}-edge completion of H, i.e,

W_{1} =^{def} 𝒞_{d^{2}}(H).

Supposing that W_{j} has been defined, the Cesàro average 𝒜_{m}(W_{j}) is an md^{2(m−1)}-regular graph with |V_{𝒜_{m}(W_{j})} | = |V_{W_{j}}| = n^{j}. Recalling (28), we have md^{2(m−1)} ⩽ n. We can therefore form the edge completion 𝒞_{n} (𝒜_{m}(W_{j})) , which is an n-regular graph, and consequently the following zigzag product is well defined.

W_{j+1} =^{def} 𝒞_{n} (𝒜_{m}(W_{j})) \begin{matrix}z \\ ◯ \end{matrix}H.  (33)

The graph W_{j+1} is d^{2}-regular and |V_{W_{j+1}}| = n|V_{𝒞_{n}(𝒜_{m}(W_{j}))} | = n^{j+1}. Moreover,

γ_{+}(W_{j+1}, d_{X}^{2}) ⩽^{(22)∧(33)} 2γ_{+}(𝒞_{n} (𝒜_{m}(W_{j})) , d_{X}^{2}) · γ_{+}(H, d_{X}^{2})^{2}
⩽^{(24)} 2γ_{+}(𝒜_{m}(W_{j}), d_{X}^{2}) · γ_{+}(H, d_{X}^{2})^{2}
⩽^{(27)} 2K max \{1, \frac{γ_{+}(W_{j}, d_{X}^{2})}{m} \} · γ_{+}(H, d_{X}^{2})^{2}

⩽^{(32)} 2K max \{1, \frac{2Kγ_{+}(H, d_{X}^{2})^{2}}{m} \} · γ_{+}(H, d_{X}^{2})^{2}
=^{(29)} 2Kγ_{+}(H, d_{X}^{2})^{2} .

This completes the inductive construction of {W_{j}}_{j=1}^{∞}.

Next, let C_{d^{2}}^{∘} be the cycle of length d^{2} in which each vertex has exactly one self-loop. We can form the zigzag product W_{j}\begin{matrix}z \\ ◯ \end{matrix}C_{d^{2}}^{∘}, which is a 9-regular graph with d^{2}n^{j} vertices. By [MN14, Lem. 2.1] we have γ_{+}(C_{d^{2}}^{∘}, d_{X}^{2}) ⩽ 12d^{4}, so we deduce from (22) and (32) that

γ_{+}(W_{j}\begin{matrix}z \\ ◯ \end{matrix}C_{d^{2}}^{∘}, d_{X}^{2}) ⩽ 2Kγ_{+}(H, d_{X}^{2})^{2} ·(12d^{4})^{2} ≲ d^{8}Kγ_{+}(H, d_{X}^{2})^{2} . (34)

Recalling that C_{9} denotes the simple cycle of length 9, define G_{j} to be the replacement product

G_{j} =^{def} (W_{j}\begin{matrix}z \\ ◯ \end{matrix}C_{d^{2}}^{∘}) \begin{matrix}r \\ ◯ \end{matrix}C_{9}.

Then G_{j} is a 3-regular graph and |V_{G_{j}}| = 9d^{2}n^{j}. By [MN14, Lem. 2.1] we have γ_{+}(C_{9}, d_{X}^{2}) ⩽ 648. It therefore follows from (23) and (34) that the desired estimate (31) holds true. □

Remark 3.3. The condition (27) is called a nonlinear spectral calculus inequality; see [MN14] for an explanation of this terminology. Looking ahead, we will rely here on the fact that such a nonlinear spectral calculus inequality is available for Hadamard spaces, as proved in [MN13].

3.5. CAT (0) and CAT (1) spaces. We need to briefly recall basic definitions related to curvature upper bounds in the sense of Aleksandrov; see [BH99] for much more on this topic.

A metric space (X, d_{X}) is a CAT (1) space if it satisfies the following conditions. First, for every x, y ∈ X with d_{X}(x, y) < π there exists a geodesic joining x to y, i.e., a curve ϕ : [0, 1] → X that satisfies d_{X}(ϕ(t), x) = td_{X}(x, y) and d_{X}(ϕ(t), y) = (1 − t)d_{X}(x, y) for every t ∈ [0, 1]. Suppose that x, y, z ∈ X satisfy

d_{X}(x, y) + d_{X}(y, z) + d_{X}(z, x) < 2π,

and that there exist geodesics ϕ_{x,y}, ϕ_{y,z}, ϕ_{z,x} : [0, 1] → X joining x to y, y to z, and z to x, respectively. Let S^{2} be the unit Euclidean sphere in ℝ^{3}, and let d_{S^{2}} denote the geodesic metric on S^{2} (thus the diameter of S^{2} equals π under this metric). As explained in [BH99], there exist a, b, c ∈ S^{2} such that d_{S^{2}}(a, b) = d_{X}(x, y), d_{S^{2}}(b, c) = d_{X}(y, z) and d_{S^{2}}(c, a) = d_{X}(z, x). Let φ_{a,b}, φ_{b,c}, φ_{c,a} : [0, 1] → S^{2} be geodesics joining a to b, b to c, and c to a, respectively. Then the remaining requirement in the definition of a CAT (1) space is that for every s, t ∈ [0, 1] we have d_{X}(ϕ_{x,y}(s), ϕ_{y,z}(t)) ⩽ d_{S^{2}}(φ_{a,b}(s), φ_{b,c}(t)).

Following Gromov [Gro01, Gro03] and Kondo [Kon12], we shall now describe a simple way to obtain CAT (1) spaces from graphs. Suppose that F is a family of connected graphs that satisfies

R_{F} =^{def} sup_{G∈F} \frac{diam(G)}{girth(G)} < ∞.  (35)

Define a metric space (U_{F}, d_{F}) as follows. U_{F} is the formal disjoint union of the one-dimensional simplicial complexes {Σ(G)}_{G∈F}, i.e.,

U_{F} =^{def} ⨆_{G∈F} Σ(G).  (36)

The metric d_{F} : U_{F} × U_{F} → [0, ∞) is defined by

d_{F}(x, y) =^{def} \{ \begin{matrix}\frac{2πd_{Σ(G)}(x,y)}{girth(G)} & if ∃G ∈ F such that x, y ∈ Σ(G), \\ 2π (R_{F} + 1) & otherwise. \end{matrix} (37)

d_{F} is indeed a metric because for every G ∈ F and every x, y ∈ Σ(G),

\frac{2πd_{Σ(G)}(x, y)}{girth(G)} ⩽ \frac{2π diam(Σ(G))}{girth(G)} ⩽^{(17)} \frac{2π (diam(G) + 1)}{girth(G)}

⩽^{(35)} \frac{2π (R_{F} girth(G)) + 1)}{girth(G)} ⩽ 2π (R_{F} + 1) . (38)

We claim that (U_{F}, d_{F}) is a CAT (1) space. Indeed, since for distinct G, H ∈ F we have d_{F}(Σ(G), Σ(H)) > 2π and for every G ∈ F the metric space (Σ(G), 2πd_{Σ(G)}/ girth(G)) is geodesic, every x, y ∈ U_{F} with d_{F}(x, y) < π can be joined by a geodesic. Moreover, if x, y, z ∈ U_{F} satisfy d_{F}(x, y)+d_{F}(y, z)+d_{F}(z, x) < 2π then necessarily x, y, z ∈ Σ(G) for some G ∈ F, in which case we have

d_{Σ(G)}(x, y) + d_{Σ(G)}(y, z) + d_{Σ(G)}(z, x) < girth(G).  (39)

It follows that

max \{d_{Σ(G)}(x, y), d_{Σ(G)}(y, z), d_{Σ(G)}(z, x)\} < \frac{girth(G)}{2}. (40)

Indeed, if (40) fails then without loss of generality we may assume that d_{Σ(G)}(z, x) ⩾ girth(G)/2. By the triangle inequality we would then have d_{Σ(G)}(x, y) + d_{Σ(G)}(y, z) ⩾ d_{Σ(G)}(z, x) ⩾ girth(G)/2, so that (39) would be violated. By (40) the geodesic triangle whose vertices are x, y, z is contained (isometrically) in a metric tree, and since metric trees are easily seen to be CAT (1) spaces (see [BH99, Sec. II.1.15]), this completes the verification that (U_{F}, d_{F}) is a CAT (1) space.

A metric space (X, d_{X}) is said to be a CAT (0) space if every two points x, y ∈ X can be joined by a geodesic, and for every x, y, z ∈ X, every geodesic ϕ : [0, 1] → X with ϕ(0) = y and ϕ(1) = z, and every t ∈ [0, 1] we have

d_{X}(x, ϕ(t))^{2} ⩽ (1 − t)d_{X}(x, y)^{2} + td_{X}(x, z)^{2} − t(1 − t)d_{X}(y, z)^{2}. (41)

A Hadamard space is a complete CAT (0) space, in which case it suffices to require the validity of (41) for t = \frac{1}{2}.

3.5.1. Nonlinear spectral calculus for CAT (0) spaces. In [MN13] we proved that CAT (0) metric spaces satisfy a nonlinear spectral calculus inequality such as (27), a tool that will be used crucially in what follows. This is a special case of a more general result that is proved in [MN13], but we will not formulate it here in order to avoid introducing terminology that is not needed for the present context (we will use the zigzag iteration of Theorem 3.2 only for CAT (0) spaces).

Theorem 3.4 ([MN13]). Let (X, d_{X}) be a CAT (0) space. Then for every m, n ∈ ℕ and every n by n symmetric and stochastic matrix M,

γ_{+}(\frac{1}{m} ∑_{t=0}^{m−1} M^{t}, d_{X}^{2}) ≲ max \{1, \frac{γ_{+}(M, d_{X}^{2})}{m} \} ,  (42)

3.6. The Euclidean cone. Let (X, d_{X}) be a metric space. The Euclidean cone over X, denoted Cone(X, d_{X}) or simply Cone(X) when the metric on X is clear from the context, is defined to be the completion of (0, ∞) × X under the metric

d_{Cone(X)}((s, x), (t, y)) =^{def} \sqrt{s^{2} + t^{2} − 2st cos (min\{π, d_{X}(x, y)\})}. (43)

This definition is due to [Ber83]; see also the exposition in [ABN86] and [BH99] (in particular, the fact that (43) satisfies the triangle inequality is proved in Proposition 5.9 of [BH99, Chapter I.5]). When X itself is complete, the completion of (0, ∞) × X under the metric defined in (43) amounts to extending the definition (43) to [0, ∞) × X and identifying all of the points in {0} × X (i.e., adding one additional point to (0, ∞) × X as the “cusp” of the cone). The results that are discussed below pass immediately from a space to its completion, so we will mostly ignore this one-point completion in the ensuing arguments.

Observe that (43) can be rewritten as follows

d_{Cone(X)}((s, x), (t, y))

= \sqrt{(s − t)^{2} + 2st (1 − cos (min\{π, d_{X}(x, y)\}))}. (44)

For the sake of later comparisons between cones, we record here the following very simple fact.

Fact 3.5. Fix D ∈ [1, ∞) and two metric space (X, d_{X}) and (Y, d_{Y}). Suppose that f : X → Y satisfies

∀ x, y ∈ X,  d_{X}(x, y) ⩽ d_{Y} (f(x), f(y)) ⩽ Dd_{X}(x, y).  (45)

Then for every distinct (s, x), (t, y) ∈ (0, ∞) × X we have

1 ⩽ \frac{d_{Cone(Y)}((s, f(x)), (t, f(y)))}{d_{Cone(X)}((s, x), (t, y))} ⩽ D.  (46)

Proof. Since ψ(a) =^{def} \sqrt{1 − cos(min\{π, a\})} = \sqrt{2} sin(min\{π/2, a/2\}) is concave and increasing on [0, ∞), and ψ(0) = 0, for every a, b ∈ [0, ∞) with a ⩽ b we have ψ(b) ⩽ bψ(a)/a. Hence by (45) we have

1 − cos (min{π, d_{X}(x, y)}) ⩽ 1 − cos (min{π, d_{Y} (f(x), f(y))})
⩽ D^{2} (1 − cos (min{π, d_{X}(x, y)})) ,

which implies (46) due to (44). □

Corollary 3.6. For every metric space (X, d_{X}) and every Banach space (Y, ∥ · ∥_{Y}) we have c_{Cone(Y)}(Cone(X)) ⩽ c_{Y} (X).

Proof. For D > c_{Y} (X) take f : X → Y that satisfies (45). Define F : (0, ∞) × X → (0, ∞) × Y by F (s, x) = (s, f(x)), and use Fact 3.5. The role of Y being a Banach space was only to ensure that we can take the scaling factor s in the definition (19) to be equal to 1. □

Proposition 3.7 below, whose proof is given in Section 5.1, asserts that c_{1}(Cone(L_{1})) < ∞, a fact that plays an important role in our proofs of Theorem 1.1 and Theorem 1.2.

Proposition 3.7. Cone(L_{1}) admits a bi-Lipschitz embedding into L_{1}.

By combining Proposition 3.7 with Corollary 3.6 we obtain the following useful corollary.

Corollary 3.8. Every metric space (X, d_{X}) satisfies

c_{1}(Cone(X)) ≲ c_{1}(X).

3.7. CAT (0) cones. An important theorem of Berestovskiĭ [Ber83] (see also [ABN86] and Theorem 3.14 of [BH99, Ch. II.3]) asserts that if X is a CAT (1) metric space then Cone(X) is a CAT (0) metric space. Consequently, we have the following lemma.

Lemma 3.9. Let F be a family of connected graphs satisfying (35), and let (U_{F}, d_{F}) be the metric space defined by (36) and (37). Then Cone(U_{F}, d_{F}) is a Hadamard space. Moreover, for every G ∈ F the metric space (Σ(G), d_{Σ(G)}) embeds into Cone(U_{F}, d_{F}) with O(1) distortion, namely,

c_{Cone(U_{F} ,d_{F})} (Σ(G), d_{Σ(G)}) ⩽ \frac{π(R_{F} + 1)}{2}.  (47)

Proof. In Section 3.5 we verified that (U_{F}, d_{F}) is a CAT (1) space, so the fact that Cone(U_{F}, d_{F}) is a Hadamard space is a consequence of Berestovskiĭ’s theorem. Fixing G ∈ F, define f : Σ(G) → (0, ∞) × U_{F} by f(x) = (1/\sqrt{2}, x). Recalling (37) and (43), for every x, y ∈ Σ(G),

d_{Cone(U_{F} ,d_{F})}(f(x), f(y)) = \sqrt{1 − cos (min \{π, \frac{2πd_{Σ(G)}(x, y)}{girth(G)}\})}.

Due to the elementary inequalities

∀ θ ∈ [0, π],  \frac{2θ^{2}}{π^{2}} ⩽ 1 − cos θ ⩽ \frac{θ^{2}}{2} ,  (48)

it follows that

d_{Cone(U_{F} ,d_{F})}(f(x), f(y)) ⩽ \frac{πd_{Σ(G)}(x, y)}{\sqrt{2} girth(G)},  (49)

and

d_{Cone(U_{F} ,d_{F})}(f(x), f(y)) ⩾ \frac{\sqrt{2}}{π} · min \{π, \frac{2πd_{Σ(G)}(x, y)}{girth(G)}\}

⩾ \frac{\sqrt{2}d_{Σ(G)}(x, y)}{R_{F} girth(G) + 1},  (50)

where the last step of (50) relies on the penultimate inequality in (38). The desired distortion estimate (47) follows from (49) and (50). □

3.8. Auxiliary families of graphs. The family of graphs to which we will apply Lemma 3.9 is given in the following definition.

Definition 3.10. Fix K ∈ (1, ∞) and denote by F_{K} all those connected graphs G with the following properties.

(1) diam(G) ⩽ K girth(G).

(2) For every S ⊆ Σ(G) with |S| ⩽ \sqrt{|V_{G}|} we have

c_{1} (Cone (S, \frac{2π}{girth(G)} · d_{Σ(G)})) < K.  (51)

We also define

𝒳_{K} =^{def} Cone (U_{F_{K}}, d_{F_{K}}) .  (52)

By Lemma 3.9 the metric space 𝒳_{K} is a Hadamard space and for every G ∈ F_{K} we have c_{𝒳_{K}} (Σ(G), d_{Σ(G)}) ≲ K. Eventually K will be chosen to be a large enough universal constant.

Definition 3.11. Fix two integers n, d ⩾ 3 and K ∈ (1, ∞). Denote by ℒ_{K}^{n,d} the set of all those connected n-vertex d-regular graphs H for which there exists a subset of edges I ⊆ E_{H} with |I| ⩽ \sqrt{n} such that if we let L be the graph (V_{H}, E_{H} ∖ I) (i.e. we remove the edges in I from H) then L has the following properties.

(a) L ∈ F_{K}.

(b) diam(L) ⩽ K log_{d} n.

(c) For every S ⊆ V_{L} with |S| ⩽ n/2 we have

|E_{L}(S, V_{L} ∖ S)| ⩾ \frac{|S|}{K}.

The following lemma asserts that a random n-vertex d-regular graph is asymptotically almost surely in ℒ_{K}^{n,d}. Due to item (a) of Definition 3.11, it follows in particular that for a large enough universal constant K, the graph family F_{K} contains many graphs.

Lemma 3.12. There exists a universal constant K ∈ (1, ∞) and for every integer d ⩾ 3 there exists C(d) ∈ (0, ∞) such that for all n ∈ ℕ,

G_{n,d} (ℒ_{K}^{n,d}) ⩾ 1 − \frac{C(d)}{\sqrt[3]{n}}.  (53)

From now on we will assume that K is the universal constant from Lemma 3.12. We shall now proceed to establish some useful corollaries of Lemma 3.12; the proof of Lemma 3.12 itself will be given in Section 7.1.

Corollary 3.13. There exists a universal constant c ∈ (0, ∞) such that for every two integers d, n ⩾ 3 we have

G_{n,d} (\{H ∈ 𝔾_{n} : γ(H, d_{𝒳_{K}}^{2} ) ⩾ c(log_{d} n)^{2}\}) ⩾ 1 − \frac{C(d)}{\sqrt[3]{n}},

where K, C(d) are the constant from Lemma 3.12 and 𝒳_{K} is as in (52).

Proof. By Lemma 3.12, it suffices to show that there exists c ∈ (0, ∞) such that if H ∈ ℒ_{K}^{n,d} then γ(H, d_{𝒳_{K}}^{2} ) ⩾ c(log_{d} n)^{2}. So, supposing that H ∈ ℒ_{K}^{n,d}, let I ⊆ E_{H} and L = (V_{H}, E_{H} ∖ I) be as in Definition 3.11.

By part (a) of Definition 3.11 we have L ∈ F_{K}, so that by Lemma 3.9 we have c_{𝒳_{K}} (V_{H}, d_{L}) ⩽ πK. This means that there exists a mapping f : V_{H} → 𝒳_{K} and a scaling factor s ∈ (0, ∞) such that

∀ x, y ∈ V_{H}, sd_{L}(x, y) ⩽ d_{𝒳_{K}} (f(x), f(y)) ⩽ πKsd_{L}(x, y).

By the definition of γ(H, d_{𝒳_{K}}^{2} ), we therefore have

\frac{1}{n^{2}} ∑_{(x,y)∈V_{H}×V_{H}} d_{L}(x, y)^{2} ≲ \frac{γ(H, d_{𝒳_{K}}^{2} )}{dn} ∑_{\{u,v\}∈E_{H}} d_{L}(x, y)^{2}.  (54)

Because L was obtained from H by deleting edges, we trivially have d_{L}(x, y) ⩾ d_{H}(x, y) for every x, y ∈ V_{H}. Since H is a d-regular graph, for a universal constant fraction of the pairs (x, y) ∈ V_{H} × V_{H} we have d_{H}(x, y) ≳ log_{d} n (for a proof of this standard and simple fact, see e.g. page 193 of [Mat97]). Hence,

\frac{1}{n^{2}} ∑_{(x,y)∈V_{H}×V_{H}} d_{L}(x, y)^{2} ⩾ \frac{1}{n^{2}} ∑_{(x,y)∈V_{H}×V_{H}} d_{H}(x, y)^{2} ≳ (log_{d} n)^{2} . (55)

Since E_{L} = E_{H} ∖ I, if {x, y} ∈ E_{H} ∖ I then d_{L}(x, y) = 1, and by part (b) of Definition 3.11 we have d_{L}(x, y) ⩽ K log_{d} n if {x, y} ∈ I. Hence,

\frac{1}{dn} ∑_{\{u,v\}∈E_{H}} d_{L}(x, y)^{2} ⩽ \frac{(|E_{H}| − |I|) + K^{2}(log_{d} n)^{2}|I|}{dn}

≲ \frac{dn + (log_{d} n)^{2}\sqrt{n}}{dn} ≲ 1,  (56)

where we used the fact that |I| ⩽ \sqrt{n}, as stipulated in Definition 3.11. The desired lower bound γ(H, d_{𝒳_{K}}^{2} ) ≳ (log_{d} n)^{2} now follows by contrasting (54) with (55) and (56). □

Corollary 3.14. Let K and C(3) be the constants from Lemma 3.12 (with d = 3). Then for every integer n > C(3)^{3} there exists an n-vertex 3-regular graph Γ_{n} such that

sup_{n>C(3)^{3}} λ_{2}(Γ_{n}) < 1,  (57)

and

sup_{n>C(3)^{3}} c_{𝒳_{K}} (V_{Γ_{n}}, d_{Γ_{n}}) < ∞,  (58)

where 𝒳_{K} is as in (52).

Proof. Since the right hand side of (53) (with d = 3) is positive, by Lemma 3.12 there exists an n-vertex 3-regular graph H such that H ∈ ℒ_{K}^{n,d}. Let L = (V_{H}, E_{H} ∖ I) be as in Definition 3.11. Since L was obtained from H by deleting edges, we can add self-loops to those vertices of V_{H} whose degree in L is less than 3 so that the resulting graph, which will be denoted Γ_{n}, is 3-regular. By part (c) of Definition (3.11), for every S ⊆ V_{Γ_{n}} with |S| ⩽ n/2 we have |E_{Γ_{n}}(S, V_{Γ_{n}} ∖ S)| ≳ |S|. By Cheeger’s inequality [Che70, AM85], this implies (57). Also, d_{Γ_{n}} = d_{L}, so (58) follows from Lemma 3.9. □

3.9. On the structure of cones over random graphs. We have already described the tools that will suffice for the proof of Theorem 1.1, but in order to also prove Theorem 1.2 we need to have a better understanding of the structure of the Euclidean cone over a random graph.

Proposition 3.15. For every integer d ⩾ 3 there exists C′(d) ∈ (0, ∞) such that for every n ∈ ℕ, if H is distributed according to G_{n,d} then with probability at least 1 − C′(d)/\sqrt[3]{n} the graph H is connected and there exist σ ∈ (0, ∞) and two subsets A_{1}, A_{2} ⊆ Σ(H) such that the following assertions hold true.

(I) A_{1} ∪ A_{2} = Σ(H).

(II) d_{Σ(H)} (A_{1} ∖ A_{2}, A_{2} ∖ A_{1}) ≳ \frac{1}{σ}. (III) c_{1} (Cone (A_{1}, σd_{Σ(H)})) ≲ 1. (IV) c_{𝒳_{K}} (Cone (A_{2}, σd_{Σ(H)})) ≲ 1. (V) c_{Cone(Σ(H),σd_{Σ(H)})} (Σ(H), d_{Σ(H)}) ≲ 1.

Here K is the constant from Lemma 3.12 and 𝒳_{K} is as in (52).

By naturally identifying the cusps of Cone(A_{1}) and Cone(A_{2}) with the cusp of Cone (Σ(H)), we have

Cone (Σ(H)) = Cone(A_{1}) ∪ Cone(A_{2}).  (59)

Proposition 3.15 therefore says that Cone (Σ(H), σd_{Σ(H)}) can be written as a (non-disjoint) union of two subsets, the first of which embeds bi-Lipschitzly into L_{1} and the second of which embeds bi-Lipschitzly into the Hadamard space 𝒳_{K}. The relevance of the remaining assertions of Proposition 3.15 will become clear in Section 3.10 below.

3.10. Unions of cones. Proposition 3.15 will be used to prove Theorem 1.2 via the following strategy. We will construct a sequence of 3-regular graphs {G_{n}}_{n=1}^{∞} satisfying (9) and such that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{𝒳_{K}}^{2} ) < ∞.

By assertion (IV) of Proposition 3.15 we therefore have

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(A_{2},σd_{Σ(H)})}^{2}) < ∞.  (60)

Since {G_{n}}_{n=1}^{∞} are necessarily also classical expanders, we will argue using Matoušek’s extrapolation [Mat97] that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{L_{1}}^{2}) < ∞,

where d_{L_{1}}(f, g) =^{def} ∥f − g∥_{1} is the standard metric on L_{1}. Assertion (III) of Proposition 3.15 therefore implies that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(A_{1},σd_{Σ(H)})}^{2}) < ∞.  (61)

We would like to combine (60) and (61) to deduce that {G_{n}}_{n=1}^{∞} are expanders with respect to Cone (Σ(H), σd_{Σ(H)}). For this purpose, we prove the following lemma in Section 5.3.

Lemma 3.16. Fix β ∈ (0, π] and n ∈ ℕ. Let (X, d_{X}) be a metric space and suppose that A, B ⊆ X satisfy A ∪ B = X and

d_{X}(A ∖ B, B ∖ A) ⩾ β.  (62)

Then every n by n symmetric stochastic matrix M = (m_{ij}) satisfies

γ_{+}(M, d_{Cone(X)}^{2}) ≲ \frac{γ_{+}(M, d_{Cone(A)}^{2}) + γ_{+}(M, d_{Cone(B)}^{2})}{β^{4}} .  (63)

By virtue of assertion (II) of Proposition 3.15, we may use Lemma 3.16 to deduce from (60) and (61) that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(Σ(H),σd_{Σ(H)})}^{2}) < ∞.

By assertion (V) of Proposition 3.15 it follows that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Σ(H)}^{2}) < ∞,

implying Theorem 1.2. It remains, of course, to explain how to construct the graphs {G_{n}}_{n=1}^{∞}; this is done in Section 4 below.

Remark 3.17. In light of (59), a positive answer to the following natural open question would yield an alternative (more general) way to carry out the above argument. Suppose that (X, d_{X}) is a metric space and A, B ⊆ X satisfy A ∪ B = X. Write d_{A} and d_{B} for the restriction of d_{X} to A and B, respectively. Let {G_{n}}_{n=1}^{∞} be a sequence of 3-regular graphs such that sup_{n∈ℕ} γ_{+}(G_{n}, d_{A}^{2}) < ∞ and sup_{n∈ℕ} γ_{+}(G_{n}, d_{B}^{2}) < ∞. Does this imply that sup_{n∈ℕ} γ_{+}(G_{n}, d_{X}^{2}) < ∞? The same question could be also asked with γ_{+}(·, ·) replaced throughout by γ(·, ·). We speculate that the answer to the above questions is negative, in which case one would ask for conditions on A and B which would imply a positive answer. Lemma 3.16 is a step in this direction.

4. Proof of Theorem 1.1 and Theorem 1.2

Here we prove Theorem 1.1 and Theorem 1.2 while assuming only the validity of Lemma 3.12, Proposition 3.15 and Lemma 3.16, which will be proven in Section 7.1, Section 7.2 and Section 5.3, respectively. Proposition 3.7, whose proof appears in Section 5.1, serves as a step towards the proof of Proposition 3.15.

4.1. A stronger theorem. We state below a theorem that directly implies both Theorem 1.1 and Theorem 1.2.

Theorem 4.1. There exist a Hadamard space (X, d_{X}) and two sequences of connected 3-regular graphs {G_{n}}_{n=1}^{∞} and {Γ_{n}}_{n=1}^{∞} satisfying lim_{n→∞} |V_{G_{n}}| = lim_{n→∞} |V_{Γ_{n}}| = ∞ and sup_{n∈ℕ} |V_{G_{n+1}}|/|V_{G_{n}}| < ∞, such that the following properties hold true.

(1) sup_{n∈ℕ} γ_{+}(G_{n}, d_{X}^{2}) < ∞.

(2) sup_{n∈ℕ} λ_{2}(Γ_{n}) < 1.

(3) sup_{n∈ℕ} c_{X} (V_{Γ_{n}}, d_{Γ_{n}}) < ∞.

(4) There exists c, C ∈ (0, ∞) and for every integer d ⩾ 1 there exists κ(d) ∈ (0, ∞) such that for every n ∈ ℕ we have

G_{n,d} (\{H ∈ 𝔾_{n} : γ(H, d_{X}^{2}) ⩾ c(log_{d} n)^{2}\}) ⩾ 1 − \frac{κ(d)}{\sqrt[3]{n}},

and

G_{n,d} (\{H ∈ 𝔾_{n}^{con} : sup_{k∈ℕ} γ_{+}(G_{k}, d_{Σ(H)}^{2}) < C\}) ⩾ 1 − \frac{κ(d)}{\sqrt[3]{n}}.

Theorem 4.1 is stronger than Theorem 1.1 and Theorem 1.2. Indeed, the estimate (6) in Theorem 1.1 is weaker than assertion (1) of Theorem 4.1 because γ_{+}(G_{n}, d_{X}^{2}) ⩾ γ(G_{n}, d_{X}^{2}). The estimate (7) in Theorem 1.1 and the estimate (10) in Theorem 1.2 follow from assertion (4) of Theorem 4.1.

The role of the graphs {Γ_{n}}_{n=1}^{∞} of Theorem 4.1 is to supply the following additional information that is not contained in Theorem 1.1 and Theorem 1.2. By assertions (2) and (3) of Theorem 4.1, we know that X contains bi-Lipschitz copies of some 3-regular (classical) expander sequence. Using (20) we deduce from assertion (3) of Theorem 4.1 γ(Γ_{n}, d_{X}^{2}) ≳ (log n)^{2}. As was discussed in the paragraph that immediately follows the statement of Theorem 1.1, we already knew that as a consequence of Theorem 1.1 there exists a sequence of 3-regular graphs {H_{n}}_{n=1}^{∞} for which γ(H_{n}, d_{X}^{2}) ≳ (log n)^{2}. Theorem 4.1 says that moreover, we can even arrange it for X to contain bi-Lipschitz copies of such an expander sequence.

4.2. Proof of Theorem 4.1. In the proof of Theorem 4.1 we will use the following easy lemma, which is in the spirit of Lemma 3.16 but simpler to prove.

Lemma 4.2. Let (X, d_{X}) be a metric space and suppose that {A_{i}}_{i∈I} are subsets of X such that

X = ⋃_{i∈I} A_{i}, and ∀ i, j ∈ I, (i ≠ j) ⟹ d_{X}(A_{i}, A_{j}) ⩾ π. (64)

Then for every n ∈ ℕ, any n by n symmetric stochastic M = (m_{ij}) satisfies

γ_{+}(M, d_{Cone(X)}^{2}) ≲ 2 sup_{i∈I} γ_{+}(M, d_{Cone(A_{i})}^{2}) ,  (65)

and

γ (M, d_{Cone(X)}^{2}) ≲ 2 sup_{i∈I} γ (M, d_{Cone(A_{i})}^{2}) ,  (66)

Proof. Let o ∈ Cone(X) denote the equivalence class obtained by identifying all the points in {0} × X, thus d_{Cone(X)}((s, x), o) = s for every (s, x) ∈ (0, ∞) × X. With a slight abuse of notation we think of o as the cusp {Cone(A_{i})}_{i∈I} as well (formally the cusps of {Cone(A)}_{i∈I} should be labeled differently, as the equivalence classes {{0} × A_{i}}_{i∈I}, but dropping this notation will not cause confusion in what follows).

For every i ∈ I define 𝔞_{i} : Cone(X) → Cone(A_{i}) by

𝔞_{i}(s, x) =^{def} \{ \begin{matrix}(s, x) & if x ∈ A_{i} \\ o & otherwise. \end{matrix}

Due to (64) every (s, x), (t, y) ∈ Cone(X) satisfy

d_{Cone(X)}((s, x), (t, y)) = ∑_{i∈I} d_{Cone(A_{i})}(𝔞_{i}(s, x), 𝔞_{i}(t, y)),  (67)

where the sum in the right-hand side of (67) contains at most two nonzero terms. Consequently,

1 ⩽ \frac{d_{Cone(X)}((s, x), (t, y))^{2}}{∑_{i∈I} d_{Cone(A_{i})}(𝔞_{i}(s, x), 𝔞_{i}(t, y))^{2}} ⩽ 2.

Every (s_{1}, x_{1}), …, (s_{n}, x_{n}), (t_{1}, y_{1}), …, (t_{n}, y_{n}) ∈ Cone(X) therefore satisfy

\frac{1}{n^{2}} ∑_{j=1}^{n} ∑_{k=1}^{n} d_{Cone(X)}((s_{j}, x_{j}), (t_{k}, y_{k}))^{2}

⩽ 2 ∑_{i∈I} \frac{1}{n^{2}} ∑_{j=1}^{n} ∑_{k=1}^{n} d_{Cone(A_{i})}(𝔞_{i}(s_{j}, x_{j}), 𝔞_{i}(t_{k}, y_{k}))^{2}

⩽ 2 ∑_{i∈I} \frac{γ_{+}(M, d_{Cone(A_{i})}^{2})}{n} ∑_{j=1}^{n} ∑_{k=1}^{n} m_{jk}d_{Cone(A_{i})}(𝔞_{i}(s_{j}, x_{j}), 𝔞_{i}(t_{k}, y_{k}))^{2}

⩽ \frac{2 sup_{i∈I} γ_{+}(M, d_{Cone(A_{i})}^{2})}{n} ∑_{j=1}^{n} ∑_{k=1}^{n} m_{jk}d_{Cone(X)}((s_{j}, x_{j}), (t_{k}, y_{k}))^{2}.

This proves (65). The proof of (66) is analogous. □

The following lemma will be used (twice) in the proof of Theorem 4.1. Its proof relies on Matoušek’s extrapolation lemma for Poincaré inequalities [Mat97], combined with an idea from [NS11].

Lemma 4.3. Fix d, n ∈ ℕ and let G be a d-regular n-vertex graph with λ_{2}(G) < 1. Then

γ(G, d_{L_{1}}^{2}) ≲ \frac{1}{(1 − λ_{2}(G))^{2}} .

Proof. Since γ(G, d_{ℝ}^{2}) = 1/(1 − λ_{2}(G)), it follows from Matoušek’s extrapolation lemma for Poincaré inequalities (see [Mat97, Prop. 3] for Matoušek’s original version and [BLMN05, Lem. 5.5] for the form that we use here) that for every f : V_{G} → L_{4} we have

\frac{1}{|V_{G}|^{2}} ∑_{(u,v)∈V_{G}×V_{G}} ∥f(u) − f(v)∥_{4}^{4}

≲ \frac{1}{(1 − λ_{2}(G))^{2}|E_{G}|} ∑_{\{u,v\}∈E_{G}} ∥f(u) − f(v)∥_{4}^{4}. (68)

The metric space (L_{1}, \sqrt{d_{L_{1}}}) admits an isometric embedding into L_{2} (see e.g. [DL97]), and L_{2} admits an isometric embedding into L_{4} (see e.g. [Woj91]), so (L_{1}, \sqrt{d_{L_{1}}}) admits an isometric embedding into L_{4}. It therefore follows from (68) that

\frac{1}{|V_{G}|^{2}} ∑_{(u,v)∈V_{G}×V_{G}} ∥f(u) − f(v)∥_{1}^{2}

≲ \frac{1}{(1 − λ_{2}(G))^{2}|E_{G}|} ∑_{\{u,v\}∈E_{G}} ∥f(u) − f(v)∥_{1}^{2}. □

Proof of Theorem 4.1. Let K be the constant from Lemma 3.12 and write X = 𝒳_{K}. The existence of the sequence of 3-regular graphs {Γ_{n}}_{n=1}^{∞}, together with assertions (2) and (3) of Theorem 4.1, is precisely Corollary 3.14. The first displayed equation in assertion (4) of Theorem 4.1 follows from Corollary 3.13.

Our goal is to construct {G_{n}}_{n=1}^{∞} using Theorem 3.2. To this end, since the assumption (27) holds true by virtue of the fact that (X, d_{X}) is a Hadamard space and Theorem 3.4, we need to prove the existence of a “base graph” that satisfies the assumption (29).

Below it will be convenient to give a name to the implicit universal constant in (42). Thus let α ∈ (1, ∞) be such that for every CAT (0) metric space (Y, d_{Y}), every m, n ∈ ℕ and every n by n symmetric and stochastic matrix M, we have

γ_{+}(\frac{1}{m} ∑_{t=0}^{m−1} M^{t}, d_{Y}^{2} ) ⩽ α max \{1, \frac{γ_{+}(M, d_{Y}^{2} )}{m} \} .  (69)

Fix from now on any sequence {W_{k}}_{k=1}^{∞} of 3-regular graphs of even cardinality that forms an expander sequence, i.e., |V_{W_{k}}| is even for every k ∈ ℕ, lim_{k→∞} |V_{W_{k}}| = ∞ and sup_{k∈ℕ} λ_{2}(W_{k}) < 1. By Lemma 4.3 we have sup_{k∈ℕ} γ(W_{k}, d_{L_{1}}^{2}) < ∞. By Lemma 3.1 there exists a sequence of 3-regular graphs {W_{k}^{∗}}_{k=1}^{∞} with

∀ k ∈ ℕ,  |V_{W_{k}^{∗}} | = 54 |V_{W_{k}}| ,  (70)

such that

β =^{def} sup_{k∈ℕ} γ_{+}(W_{k}^{∗}, d_{L_{1}}^{2}) < ∞.  (71)

Choose the minimum k ∈ ℕ such that

|V_{W_{k}^{∗}}| ⩾ 3^{48αβ^{2}K^{4}}.  (72)

Note that since α, β, K are all universal constants, so are k and |V_{W_{k}^{∗}}|. Define a subfamily F_{K}^{∗} of the graph family F_{K} by

F_{K}^{∗} =^{def} {G ∈ F_{K} : |V_{G}| ⩾ 4|V_{W_{k}^{∗}}|^{2}} .  (73)

Fix G ∈ F_{K}^{∗} and two mappings

φ, ψ : V_{W_{k}^{∗}} → Cone (Σ(G), \frac{2π}{girth(G)} · d_{Σ(G)}) .  (74)

Observe that

|φ (V_{W_{k}^{∗}}) ∪ ψ (V_{W_{k}^{∗}})| ⩽ 2 |V_{W_{k}^{∗}} | ⩽^{(73)} \sqrt{|V_{G}|}.

Consequently, there exists S ⊆ Σ(G) with |S| ⩽ \sqrt{|V_{G}|} such that

φ (V_{W_{k}^{∗}}) ∪ ψ (V_{W_{k}^{∗}}) ⊆ Cone (S, \frac{2π}{girth(G)} · d_{Σ(G)}) .

By assertion (2) of Definition 3.10, it follows that

c_{1} (φ (V_{W_{k}^{∗}}) ∪ ψ (V_{W_{k}^{∗}}) , d_{Cone}_{(Σ(G), \frac{2π}{girth(G)} ·d_{Σ(G)})}) ⩽ K. (75)

Since (75) holds true for every φ, ψ as in (74), it follows from the definition of β in (71) that

γ_{+}(W_{k}^{∗}, d_{Cone(Σ(G), \frac{2π}{girth(G)} ·d_{Σ(G)})}^{2}) ⩽ βK^{2}.  (76)

Recalling the definitions (36) and (37), U_{F_{K}^{∗}} is the disjoint union of {Σ(G)}_{G∈F_{K}^{∗}}, where the distances (in terms of d_{F_{K}^{∗}}, which is the same as the restriction to U_{F_{K}^{∗}} of d_{F_{K}}) between different sets in this disjoint union being at least 2π. We are therefore allowed to use Lemma 4.2, thus concluding from (76) that

γ_{+}(W_{k}^{∗}, d_{Cone(U_{F^{∗}_{K}} ,d_{F^{∗}_{K}} )}^{2}) ⩽ 2βK^{2} ⩽^{(72)} \sqrt{\frac{1}{2α} ⌊\frac{log |V_{W_{k}^{∗}} |}{3 log 3}⌋}. (77)

By the definition of α in (69), due to (77) (which corresponds to condition (29)) we can apply Theorem 3.2 to the CAT (0) metric space Cone (U_{F_{K}^{∗}}, d_{F_{K}^{∗}}) and the “base graph” H = W_{k}^{∗}, thus obtaining a sequence of 3-regular graphs {F_{n}}_{n=1}^{∞} with

∀ n ∈ ℕ,  |V_{F_{n}}| = 81 |V_{W_{k}^{∗}} |^{n} =^{(70)} 81 · (54 |V_{W_{k}}|)^{n} ,  (78)

and

sup_{n∈ℕ} γ_{+}(F_{n}, d_{Cone(U_{F^{∗}_{K}} ,d_{F^{∗}_{K}} )}^{2}) < ∞.  (79)

Finally, define G_{n} =^{def} F_{n}^{∗}, where F_{n}^{∗} is obtained from F_{n} by Lemma 3.1 (we are allowed to apply Lemma 3.1 here since by (78) we know that |V_{F_{n}}| is even). Then |V_{G_{n}}| = 54 |V_{F_{n}}| for every n ∈ ℕ. Moreover, by Lemma 3.1 for every n ∈ ℕ we have

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(U_{F^{∗}_{K}} ,d_{F^{∗}_{K}} )}^{2}) ≲ sup_{n∈ℕ} γ(F_{n}, d_{Cone(U_{F^{∗}_{K}} ,d_{F^{∗}_{K}} )}^{2})

⩽ sup_{n∈ℕ} γ_{+}(F_{n}, d_{Cone(U_{F^{∗}_{K}} ,d_{F^{∗}_{K}} )}^{2}) < ∞. (80)

Due to (79) the graphs {F_{n}}_{n=1}^{∞} are necessarily also classical expanders, i.e., sup_{n∈ℕ} λ_{2}(F_{n}) < 1. By another application of Lemma 4.3 we therefore conclude that sup_{n∈ℕ} γ(F_{n}, d_{L_{1}}^{2}) < ∞, and by Lemma 3.1 this implies that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{L_{1}}^{2}) ≲ sup_{n∈ℕ} γ(F_{n}, d_{L_{1}}^{2}) < ∞.  (81)

If H ∈ F_{K} ∖ F_{K}^{∗} then |V_{H}| < 4|V_{W_{k}^{∗}}|^{2}. Hence, as explained in Section 3.2 we have

sup_{H∈F_{K}∖F_{K}^{∗}} c_{1}(Σ(H)) < ∞.

By Corollary 3.8 we therefore have

sup_{H∈F_{K}∖F_{K}^{∗}} c_{1} (Cone (Σ(H), \frac{2π}{girth(H)} · d_{Σ(H)})) < ∞. (82)

In conjunction with (81), it follows from (82) that

sup_{H∈F_{K}∖F_{K}^{∗}} sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(Σ(H), \frac{2π}{girth(H)} ·d_{Σ(H)})}^{2}) < ∞.  (83)

Recalling the definitions (36) and (37), U_{F_{K}} is the disjoint union of U_{F_{K}^{∗}} and {Σ(H)}_{H∈F_{K}∖F_{K}^{∗}} with the distances (in terms of d_{F_{K}}) between different sets in this disjoint union being at least 2π. We can therefore apply Lemma 4.2 to (80) and (83), yielding assertion (1) of Theorem 4.1.

It remains to justify the second displayed equation in assertion (4) of Theorem 4.1. This is done following the strategy that was sketched in Section 3.10. Let H be a connected 3-regular n-vertex graph that satisfies the conclusion of Proposition 3.15. By Proposition 3.15 we know that the G_{n,d}-probability that this occurs is at least 1−C′(d)/\sqrt[3]{n}. Continuing here with the notation of Proposition 3.15, it follows from assertion (1) of Theorem 4.1 (which we already proved), combined with assertion (IV) of Proposition 3.15 that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(A_{2},σd_{Σ(H)})}^{2}) ≲ 1.  (84)

Due to (81), assertion (III) of Proposition 3.15 implies that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(A_{1},σd_{Σ(H)})}^{2}) ≲ 1.  (85)

By assertions (I) and (II) of Proposition 3.15, we may use Lemma 3.16 to deduce from (84) and (85) that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Cone(Σ(H),σd_{Σ(H)})}^{2}) ≲ 1.

By assertion (V) of Proposition 3.15 it follows that

sup_{n∈ℕ} γ_{+}(G_{n}, d_{Σ(H)}^{2}) ≲ 1.  □

5. On the Lipschitz structure of Euclidean cones

Here we study various aspects of the Lipschitz structure of Euclidean cones. In particular, we will prove Proposition 3.7 and Lemma 3.16. We will also establish several additional geometric results of independent interest, yielding as a side product progress on a question that we raised in [MN04]. Despite the fact that in this paper the value of universal constants is mostly insignificant, when proving results such as Proposition 3.7 we will attempt to state reasonably good (though still suboptimal) explicit distortion bounds, because such geometric results are interesting in their own right and might be useful elsewhere.

Before proceeding we record for future use the following simple facts.

Lemma 5.1. Let (X, d_{X}) be a metric space. For every s, t ∈ (0, ∞) and x, y ∈ X we have

d_{Cone(X)}((s, x), (t, y)) ⩾ max\{s, t\} · sin (min \{\frac{π}{2} , d_{X}(x, y)\}) . (86)

Proof. If d_{X}(x, y) ⩾ π/2 then cos (min{π, d_{X}(x, y)}) ⩽ 0 and it follows from (43) that d_{Cone(X)}((s, x), (t, y)) ⩾ \sqrt{s^{2} + t^{2}} ⩾ max\{s, t\}. We may therefore assume that d_{X}(x, y) < π/2 and s ⩾ t. The minimum of the function t ↦ s^{2} + t^{2} − 2st cos (d_{X}(x, y)) is attained at t = s cos (d_{X}(x, y)), implying (86). □

Lemma 5.2. Let (X, d_{X}) be a bounded metric space. Suppose that f : X → (0, ∞) is Lipschitz and define F : Cone(X) → Cone(X) by

F (s, x) =^{def} (f(x)s, x).

Then F is Lipschitz with

∥F ∥_{Lip} ⩽ \sqrt{diam(X)^{2} · ∥f∥_{Lip}^{2} + 2∥f∥_{∞}^{2}}.

Proof. Fix x, y ∈ X and set θ =^{def} min {π, d_{X}(x, y)}. For s, t ∈ (0, ∞) with s ⩽ t we have

|f(x)s − f(y)t| ⩽ |f(x) − f(y)|s + |f(y)| · |s − t|

⩽ ∥f∥_{Lip}d_{X}(x, y)s + ∥f∥_{∞}|s − t|

⩽ \frac{diam(X)∥f∥_{Lip}}{π} θ\sqrt{st} + ∥f∥_{∞}|s − t|

⩽ \frac{diam(X)∥f∥_{Lip}}{\sqrt{2}} \sqrt{st(1 − cos θ)} + ∥f∥_{∞}|s − t|, (87)

where in (87) we used (48). By squaring (87) we see that

(f(x)s − f(y)t)^{2} ⩽ 2∥f∥_{∞}^{2}(s−t)^{2} +diam(X)^{2}∥f∥_{Lip}^{2}st(1−cos θ), (88)

and therefore,

d_{Cone(X)}(F (s, x), F (t, y))^{2}

=^{(44)} (f(x)s − f(y)t)^{2} + 2f(x)f(y)st(1 − cos θ)

⩽^{(88)} 2∥f∥_{∞}^{2}(s − t)^{2} + (diam(X)^{2}∥f∥_{Lip}^{2} + 2∥f∥_{∞}^{2}) st(1 − cos θ)

⩽^{(44)} (diam(X)^{2}∥f∥_{Lip}^{2} + 2∥f∥_{∞}^{2}) d_{Cone(X)}((s, x), (t, y))^{2}.  □

Lemma 5.3. Let (X, d_{X}) be a metric space and suppose that (s, x) and (t, y) are distinct points in Cone(X). Then

\frac{1}{3} ⩽ \frac{d_{Cone(X)}((s, x), (t, y))}{max \{|s − t|, max\{s, t\} · \sqrt{2 (1 − cos (min\{π, d_{X}(x, y)\}))}\}} ⩽ \sqrt{2}.

Proof. Denoting θ =^{def} min {π, d_{X}(x, y)}, it follows from (44) that

d_{Cone(X)}((s, x), (t, y)) = \sqrt{(s − t)^{2} + 2st(1 − cos θ)}.  (89)

The desired upper bound on d_{Cone(X)}((s, x), (t, y)) is therefore immediate from (89), using \sqrt{st} ⩽ max\{s, t\}. Since \sqrt{st} ⩾ max\{s, t\} − |s − t|, it follows from (89) that

d_{Cone(X)}((s, x), (t, y))

⩾ max \{|s − t|, max\{s, t\} · \sqrt{2(1 − cos θ)} − |s − t|\sqrt{2(1 − cos θ)}\}

⩾ \frac{max \{|s − t|, max\{s, t\} · \sqrt{2(1 − cos θ)}\}}{1 + \sqrt{2(1 − cos θ)}},

yielding the desired lower bound on d_{Cone(X)}((s, x), (t, y)). □

5.1. The Euclidean cone over L_{1}. Our goal here is to prove Proposition 3.7. In preparation for this we first show that if one equips L_{1} with the metric ρ(x, y) = min{1, ∥x − y∥_{1}}, then the resulting metric space admits a bi-Lipschitz embedding into L_{1}. In [MN04] we showed that the metric space (L_{2}, min{1, ∥x − y∥_{2}}) admits a bi-Lipschitz embedding into L_{2} but left open the question whether c_{p}(L_{p}, min{1, ∥x−y∥_{p}}) < ∞ for p ∈ [1, 2); Lemma 5.4 below shows that this is indeed the case when p = 1, but the question remains open for p ∈ (1, 2). As explained in [MN04, Rem. 5.12], for every p ∈ (2, ∞] the metric space (L_{p}, min{1, ∥x − y∥_{p}}) does not admit a bi-Lipschitz embedding into L_{q} for any q ∈ [1, ∞).

Lemma 5.4 (truncated L_{1} embeds into L_{1}). For every M ∈ (0, ∞) there exists a mapping T_{M} : L_{1} → L_{1} such that

• ∥T_{M}(x)∥_{1} = M for every x ∈ L_{1},

• For every distinct x, y ∈ L_{1},
1 − \frac{1}{e} ⩽ \frac{∥T_{M}(x) − T_{M}(y)∥_{1}}{min\{M, ∥x − y∥_{1}\}} ⩽ 1.  (90)

Proof. Fix an integer n ⩾ 2. For every A ⊆ {1, …, n} the Walsh function W_{A} : {0, 1}^{n} → {−1, 1} is defined as usual by

∀ z = (z_{1}, …, z_{n}) ∈ {0, 1}^{n}, W_{A}(z) =^{def} ∏_{j∈A}(−1)^{z_{j}}.

For λ ∈ [1/2, 1] observe that every x, y ∈ {0, 1}^{n} satisfy

1 − (2λ − 1)^{∥x−y∥_{1}} = 1 − ∏_{j=1}^{n} (λ + (−1)^{x_{j}+y_{j}}(1 − λ))

= ∑_{A⊆{1,...,n}} λ^{n−|A|}(1 − λ)^{|A|} (1 − W_{A}(x + y))

= ∑_{A⊆{1,...,n}} λ^{n−|A|}(1 − λ)^{|A|} |W_{A}(x) − W_{A}(y)| .  (91)

Let {h_{A}}_{A⊆{1,...,n}} ⊆ L_{1} be disjointly supported functions with unit L_{1} norm, and define Φ_{M}^{n} : {0, 1}^{n} → L_{1} by setting for every z ∈ {0, 1}^{n},

Φ_{M}^{n}(z) =^{def} M ∑_{A⊆\{1,...,n\}} (\frac{1 + e^{−1/M}}{2} )^{n−|A|} (\frac{1 − e^{−1/M}}{2} )^{|A|} W_{A}(z)h_{A}.

Then ∥Φ_{M}^{n}(z)∥_{1} = M for every z ∈ {0, 1}^{n}, and it follows from (91) that for every x, y ∈ {0, 1}^{n} we have

\frac{∥Φ_{M}^{n}(x) − Φ_{M}^{n}(y)∥_{1}}{min\{M, ∥x − y∥_{1}\}} = \frac{M (1 − e^{−∥x−y∥_{1}/M})}{min\{M, ∥x − y∥_{1}\}} ∈ [1 − \frac{1}{e}, 1] . (92)

This proves the existence of the desired embedding for the metric space ({0, 1}^{n}, min{M, ∥ · ∥_{1}}); we pass to all of L_{1} via the following standard approximation argument. If S ⊆ L_{1} is finite and ε ∈ (0, 1) then by approximating by step functions we can choose d, K, Q ∈ ℕ and k_{1}, … k_{d} : S → {1, …, K} such that for every x, y ∈ S,

\frac{1}{Q} ∑_{j=1}^{d} |k_{i}(x) − k_{i}(y)| ⩽ ∥x − y∥_{1} ⩽ \frac{1 + ε}{Q} ∑_{j=1}^{d} |k_{i}(x) − k_{i}(y)|. (93)

Write n = dK and define ψ : S → {0, 1}^{n} by

∀ x ∈ S, ψ(x) =^{def} ∑_{i=1}^{d} ∑_{j=(i−1)K+1}^{(i−1)K+k_{i}(x)} e_{j},

where e_{1}, …, e_{n} is the standard basis of ℝ^{n}. Then by (93),

∀ x, y ∈ S,  \frac{Q}{1 + ε}∥x − y∥_{1} ⩽ ∥ψ(x) − ψ(y)∥_{1} ⩽ Q∥x − y∥_{1}. (94)

Define F_{ε} : S → L_{1} by

F_{ε} =^{def} \frac{1}{Q}Φ_{QM}^{n} ∘ ψ.

Then ∥F_{ε}(x)∥_{1} = M for every x ∈ S, and by (92) and (94) we have

\frac{1}{1 + ε} (1 − \frac{1}{e}) ⩽ \frac{∥F_{ε}(x) − F_{ε}(y)∥_{1}}{min\{M, ∥x − y∥_{1}\}} ⩽ 1

for all distinct x, y ∈ S. Since this holds for every finite S ⊆ L_{1} and every ε ∈ (0, 1), the existence of the mapping T_{M} from the statement of Lemma 5.4 now follows from a standard ultrapower argument (see [Kap09, Ch. 9] for background on ultrapowers of metric spaces), using the fact that any ultrapower of L_{1} is an L_{1}(μ) space [Hei80]. □

Remark 5.5. As explained in [MN04, Rem. 5.4], it follows formally from Lemma 5.4 that there exists a universal constant C ∈ (0, ∞) such that if ω : [0, ∞) → [0, ∞) is a concave nondecreasing function with ω(0) = 0 and ω(t) > 0 for t > 0 then the metric space (L_{1}, ω(∥x−y∥_{1})) embeds into L_{1} with distortion at most C. The analogous assertion with L_{1} replaced by L_{p} for p ∈ (1, 2) remains open.

Proof of Proposition 3.7. Let T_{π} : L_{1} → L_{1} be the mapping constructed in Lemma 5.4 (with M = π). Define F : Cone(L_{1}) → L_{1} by

∀(s, x) ∈ (0, ∞) × L_{1},  F (s, x) =^{def} sT_{π}(x).  (95)

Fix (s, x), (t, y) ∈ (0, ∞) × L_{1} and assume without loss of generality that t ⩾ s. Recalling (44), we have

d_{Cone(L_{1})}((s, x), (t, y)) = \sqrt{(s − t)^{2} + 2st(1 − cos θ)},  (96)

where

θ =^{def} min{π, ∥x − y∥_{1}}.  (97)

Moreover,

∥T_{π}(x)∥_{1} = ∥T_{π}(y)∥_{1} = π  (98)

and (90) becomes

(1 − \frac{1}{e}) θ ⩽ ∥T_{π}(x) − T_{π}(y)∥_{1} ⩽ θ.  (99)

Using also (48), we therefore have

∥F (s, x) − F (t, y)∥_{1} = ∥sT_{π}(x) − tT_{π}(y)∥_{1}  (100)
⩽ s∥T_{π}(x) − T_{π}(y)∥_{1} + (t − s)∥T_{π}(y)∥_{1} (101)
⩽ sθ + π(t − s)  (102)
⩽ \frac{π}{2} \sqrt{2st(1 − cos θ)} + π(t − s)  (103)
⩽ \frac{π\sqrt{5}}{2}\sqrt{2st(1 − cos θ) + (s − t)^{2}}  (104)
= \frac{π\sqrt{5}}{2}d_{Cone(L_{1})}((s, x), (t, y)),  (105)

where in (100) we used (95), in (101) we used the triangle inequality in L_{1}, in (102) we used (98) and the rightmost inequality in (99), in (103) we used the leftmost inequality in (48) and that s ⩽ \sqrt{st}, in (104) we used the Cauchy-Schwarz inequality, and in (105) we used (96).

Next, we claim that

∥F (s, x) − F (t, y)∥_{1} ⩾ \frac{π(e − 1)}{\sqrt{4π^{2}e^{2} + (e − 1)^{2}}} d_{Cone(L_{1})}((s, x), (t, y)).

(106)

Once proved, (106) in conjunction with (105) would imply that

c_{1}(Cone(L_{1})) ⩽ \frac{\sqrt{20π^{2}e^{2} + 5(e − 1)^{2}}}{2(e − 1)} ⩽ 11.17,  (107)

thus completing the proof of Proposition 3.7.

We prove (106) by distinguishing between two cases as follows.

Case 1: t − s ⩾ \frac{e−1}{2πe}tθ. Since t ⩾ \sqrt{st} this assumption combined with the rightmost inequality appearing in (48) implies that

t − s ⩾ \frac{e − 1}{2πe}\sqrt{2st(1 − cos θ)}.  (108)

Consequently,

d_{Cone(L_{1})}((s, x), (t, y)) ⩽^{(96)∧(108)} (t − s)\frac{\sqrt{4π^{2}e^{2} + (e − 1)^{2}}}{e − 1} , (109)

implying the desired inequality (106) as follows.

∥F (s, x) − F (t, y)∥_{1} =^{(95)} ∥sT_{π}(x) − tT_{π}(y)∥_{1}
⩾ t∥T_{π}(y)∥_{1} − s∥T_{π}(x)∥_{1}
=^{(98)} π(t − s)
⩾^{(109)} \frac{π(e − 1)}{\sqrt{4π^{2}e^{2} + (e − 1)^{2}}} d_{Cone(L_{1})}((s, x), (t, y)).

Case 2: we have

t − s < \frac{e − 1}{2πe}tθ.  (110)

It follows that

∥F (s, x) − F (t, y)∥_{1}  =^{(95)} ∥sT_{π}(x) − tT_{π}(y)∥_{1}
⩾ t∥T_{π}(y) − T_{π}(x)∥_{1} − (t − s)∥T_{π}(x)∥_{1}
⩾^{(99)∧(98)} (1 − \frac{1}{e}) tθ − π(t − s)
>^{(110)}  \frac{e − 1}{2e}tθ.  (111)

At the same time, since st ⩽ t^{2}, it follows from (96), combined with (110) and the rightmost inequality appearing in (48), that

d_{Cone(L_{1})}((s, x), (t, y)) ⩽ tθ\frac{\sqrt{4π^{2}e^{2} + (e − 1)^{2}}}{2πe} .  (112)

In combination with (111), it follows from (112) that the desired inequality (106) holds true in Case 2 as well, thus completing the proof of Proposition 3.7. □

5.2. Snowflakes of cones. For α ∈ (0, 1], the α-snowflake of a metric space (X, d) is defined to be the metric space (X, d^{α}). It is well known that the \frac{1}{2}-snowflake of L_{1} embeds isometrically into L_{2} (see e.g. [DL97] or [Nao10, Sec. 3]). Consequently, Proposition 3.7 implies that the \frac{1}{2}-snowflake of Cone(L_{1}) admits a bi-Lipschitz distortion into L_{2}. Proposition 5.6 below yields an alternative proof of this fact. Note that Proposition 5.6 does not imply Proposition 3.7 since by [KV05] there exist metric spaces which do not admit a bi-Lipschitz embedding into L_{1} yet their \frac{1}{2}-snowflake admits an isometric embedding into L_{2}.

In the proof of Theorem 4.1 (most significantly, in the proofs of Lemma 3.12 and Proposition 3.15) we use Proposition 3.7, but one can use mutatis mutandis Proposition 5.6 instead. We include Proposition 5.6 here due to its intrinsic interest, but it will not be used in this paper other than as an indication of an alternative approach to the parts of the proof of Theorem 4.1 eluded to above (note, however, that the approach below yields a better bound; see Remark 5.7). Readers who familiarized themselves with the contents of Section 5.1 can skip the present section if they only wish to understand the proof of Theorem 4.1.

Proposition 5.6. For every separable metric space (X, d_{X}) and every α ∈ (0, 1] we have

c_{2} (Cone(X), d_{Cone(X)}^{α}) ≲ c_{2} (X, d_{X}^{α}) .  (113)

Proof. By a classical theorem of Schoenberg [Sch38] (see also [WW75]) there exists a mapping h_{α} : ℝ → L_{2} with h_{α}(0) = 0 such that

∀ s, t ∈ ℝ,  ∥h_{α}(s) − h_{α}(t)∥_{2} = |s − t|^{α}.  (114)

Also, as shown in [MN04, Lem. 5.2], there exists τ_{α} : L_{2} → L_{2} such that every distinct x, y ∈ L_{2} satisfy

∥τ_{α}(x)∥_{2} = ∥τ_{α}(y)∥_{2} = \frac{π^{α}}{\sqrt{2}},  (115)

and

\sqrt{1 − \frac{1}{e}} ⩽ \frac{∥τ_{α}(x) − τ_{α}(y)∥_{2}}{min\{π^{α}, ∥x − y∥_{2}\}} ⩽ 1.  (116)

Writing D =^{def} c_{2} (X, d_{X}^{α}), there exists f : X → L_{2} such that

∀ x, y ∈ X  \frac{d_{X}(x, y)^{α}}{D} ⩽ ∥f(x) − f(y)∥_{2} ⩽ d_{X}(x, y)^{α}.  (117)

We can now define an embedding ϕ : Cone(X) → L_{2} ⊗ L_{2} by

∀(s, x) ∈ (0, ∞) × X,  ϕ(s, x) =^{def} h_{α}(s) ⊗ τ_{α}(f(x)).  (118)

Here the tensor product L_{2} ⊗ L_{2} is equipped with the Hilbert space tensor product (Hilbert-Schmidt) norm. Thus L_{2} ⊗ L_{2} is isometric to L_{2} and ∥x ⊗ y∥_{L_{2}⊗L_{2}} = ∥x∥_{2} · ∥y∥_{2} for every x, y ∈ L_{2}. Consequently, for every a, b, x, y ∈ L_{2} we have

∥a ⊗ x − b ⊗ y∥_{L_{2}⊗L_{2}}^{2} = ∥a∥_{2}^{2} · ∥x∥_{2}^{2} + ∥b∥_{2}^{2} · ∥y∥_{2}^{2}

− \frac{1}{2} (∥a∥_{2}^{2} + ∥b∥_{2}^{2} − ∥a − b∥_{2}^{2}) (∥x∥_{2}^{2} + ∥y∥_{2}^{2} − ∥x − y∥_{2}^{2}) . (119)

Fix x, y ∈ X and s, t ∈ (0, ∞) with t ⩾ s. Since h_{α}(0) = 0 it follows from (114) that ∥h_{α}(s)∥_{2} = s^{α} and ∥h_{α}(t)∥_{2} = t^{α}. In conjunction with (115), this shows that due to (114), (118) and (119),

∥ϕ(s, x) − ϕ(t, y)∥_{2}^{2}  (120)

= \frac{π^{2α}}{2} (s^{2α} + t^{2α})

− \frac{1}{2} (s^{2α} + t^{2α} − (t − s)^{2α}) (π^{2α} − ∥τ_{α}(f(x)) − τ_{α}(f(y))∥_{2}^{2})

= \frac{π^{2α}}{2} (t − s)^{2α} + \frac{s^{2α} + t^{2α} − (t − s)^{2α}}{2} ∥τ_{α}(f(x)) − τ_{α}(f(y))∥_{2}^{2} .

Let θ ∈ [0, π] be given by

θ =^{def} min{π, d_{X}(x, y)}.  (121)

Recalling (44), we therefore have

d_{Cone(X)}((s, x), (t, y)) = \sqrt{(s − t)^{2} + 2st(1 − cos θ)},  (122)

and

∥τ_{α}(f(x)) − τ_{α}(f(y))∥_{2} ⩽^{(116)∧(117)} θ^{α} ⩽^{(48)} \frac{π^{α}}{2^{α/2}} (1 − cos θ)^{α/2}. (123)

To bound (120) from above we also note the following elementary inequality, which holds for every s ∈ [0, ∞), t ∈ [s, ∞) and α ∈ [0, 1].

s^{2α} + t^{2α} − (t − s)^{2α} ⩽ 2(st)^{α}.  (124)

To verify the validity of (124), normalization by t^{2α} shows that it suffices to check that ψ(α) = 2u^{α} − u^{2α} − 1 + (1 − u)^{2α} ⩾ 0 for every fixed u ∈ (0, 1). Indeed, ψ′(α) = 2u^{α}(1−u^{α}) log u+2(1−u)^{2α} log(1−u) ⩽ 0, so ψ(α) ⩾ ψ(1) = 0.

By substituting (123) and (124) into (120) we have

∥ϕ(s, x) − ϕ(t, y)∥_{2} ⩽ \frac{π^{α}}{2^{α+1/2}} \sqrt{2^{2α}|s − t|^{2α} + (2st(1 − cos θ))^{α}}

⩽ \frac{π^{α} (2^{2α/(1−α)} + 1)^{1−α}}{2^{α+1/2}} d_{Cone(X)}((s, x), (t, y))^{α}

≲ d_{Cone(X)}((s, x), (t, y))^{α},  (125)

where the penultimate step of (125) uses Hölder’s inequality and (122). To bound (120) from below, observe first that

∥τ_{α}(f(x)) − τ_{α}(f(y))∥_{2} ⩾^{(116)∧(117)} \sqrt{1 − \frac{1}{e}} · min \{π^{α}, \frac{d_{X}(x, y)^{α}}{D} \}

⩾^{(121)} \frac{\sqrt{e − 1}}{D\sqrt{e}}θ^{α} ⩾^{(48)} \frac{2^{α/2}\sqrt{e − 1}}{D\sqrt{e}}(1 − cos θ)^{α/2}. (126)

Substituting (126) and the trivial estimate s^{2α} + t^{2α} − (t − s)^{2α} ⩾ s^{2α} into (120) shows that

∥ϕ(s, x) − ϕ(t, y)∥_{2} ⩾ \frac{\sqrt{\frac{π^{2α}eD^{2}}{e−1} |s − t|^{2α} + (2s^{2}(1 − cos θ))^{α}}}{D (\frac{2e}{e−1})^{α/2}} . (127)

But, recalling (122), since st ⩽ 2s^{2} + (s − t)^{2} we have

d_{Cone(X)}((s, x), (t, y))^{α} ⩽ (3(s − t)^{2} + 2s^{2}(1 − cos θ))^{α/2}

⩽ \sqrt{3^{α}|s − t|^{2α} + (2s^{2}(1 − cos θ))^{α}} ≲^{(127)} D∥ϕ(s, x) − ϕ(t, y)∥_{2}. (128)

Due to (125) and (128) the proof of Proposition 5.6 is complete. □

Remark 5.7. When α = \frac{1}{2} and D = 1, a more careful analysis of the proof of Proposition 5.6 yields the estimate

c_{2} (Cone(L_{1}), d_{Cone(L_{1})}^{1/2}) ⩽ 2.574.

We omit the (tedious) proof of this estimate, noting that it is better than the bound that follows from an application of Proposition 3.7 (specifically, recall (107)) and the fact that the \frac{1}{2}-snowflake of L_{1} embeds isometrically into L_{2}.

5.3. Poincaré inequalities with respect to unions of cones. Our goal here is to prove Lemma 3.16. Before doing so, we record the following simple observation.

Lemma 5.8. Fix λ, κ ∈ (0, ∞) and n ∈ ℕ. Let (A, d_{A}), (B, d_{B}) and (X, d_{X}) be metric spaces. Suppose that there exist λ-Lipschitz mappings 𝔞 : X → A and 𝔟 : X → B such that for every x, y ∈ X,

\sqrt{d_{A}(𝔞(x), 𝔞(y))^{2} + d_{B}(𝔟(x), 𝔟(y))^{2}} ⩾ \frac{d_{X}(x, y)}{κ}.  (129)

Then every n by n symmetric stochastic matrix M = (m_{ij}) satisfies

γ_{+}(M, d_{X}^{2}) ⩽ (κλ)^{2} (γ_{+}(M, d_{A}^{2}) + γ_{+}(M, d_{B}^{2})) .  (130)

Proof. For every x_{1}, …, x_{n}, y_{1}, …, y_{n} ∈ X we have

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{X}(x_{i}, y_{j})^{2}

⩽^{(129)} \frac{κ^{2}}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} (d_{A}(𝔞(x_{i}), 𝔞(y_{j}))^{2} + d_{B}(𝔟(x_{i}), 𝔟(y_{j}))^{2}) . (131)

By the definition of γ_{+}(M, d_{A}^{2}) and γ_{+}(M, d_{B}^{2}), combined with the fact that 𝔞 and 𝔟 are λ-Lipschitz,

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} (d_{A}(𝔞(x_{i}), 𝔞(y_{j}))^{2} + d_{B}(𝔟(x_{i}), 𝔟(y_{j}))^{2})

⩽ \frac{γ_{+}(M, d_{A}^{2})}{n} ∑_{i=1}^{n} ∑_{j=1}^{n} m_{ij}d_{A}(𝔞(x_{i}), 𝔞(y_{j}))^{2}

+ \frac{γ_{+}(M, d_{B}^{2})}{n} ∑_{i=1}^{n} ∑_{j=1}^{n} m_{ij}d_{B}(𝔟(x_{i}), 𝔟(y_{j}))^{2}

⩽ \frac{λ^{2} (γ_{+}(M, d_{A}^{2}) + γ_{+}(M, d_{B}^{2}))}{n} ∑_{i=1}^{n} ∑_{j=1}^{n} m_{ij}d_{X}(x_{i}, y_{j})^{2}.

In combination with (131), this completes the proof of Lemma 5.8. □

Remark 5.9. The identical argument shows that (130) also holds true with the quantities γ_{+}(M, d_{X}^{2}) , γ_{+}(M, d_{A}^{2}) , γ_{+}(M, d_{B}^{2}) replaced by the quantities γ (M, d_{X}^{2}) , γ (M, d_{A}^{2}) , γ (M, d_{B}^{2}), respectively.

Proof of Lemma 3.16. Since the Euclidean cone over (X, d_{X}) is isometric to the Euclidean cone over the metric space (X, min{π, d_{X}}), it suffices to prove (63) under the additional assumption

diam(X) ⩽ π.  (132)

Let o denote the cusp of Cone(X), i.e, the equivalence class obtained by identifying all of the points in {0}×X. Thus d_{Cone(X)}((s, x), o) = s for every (s, x) ∈ (0, ∞) × X. Define 𝔞 : Cone(X) → Cone(A) and 𝔟 : Cone(X) → Cone(B) by setting for every (s, x) ∈ (0, ∞) × X,

𝔞(s, x) =^{def} (d_{X}(x, B ∖ A)s, x) and 𝔟(s, x) =^{def} (d_{X}(x, A ∖ B)s, x).

We first show that 𝔞 and 𝔟 are \sqrt{3}π-Lipschitz. By symmetry it suffices check this for 𝔞, i.e., to show that for every s, t ∈ (0, ∞) and x, y ∈ X,

d_{Cone(A)}(𝔞(s, x), 𝔞(t, y)) ⩽ \sqrt{3}πd_{Cone(X)}((s, x), (t, y)).  (133)

(133) is trivial if d_{X}(x, B ∖A) = d_{X}(y, B ∖A) = 0. If d_{X}(x, B ∖A) > 0 and d_{X}(y, B ∖ A) = 0 then

d_{Cone(A)}(𝔞(s, x), 𝔞(t, y))
= d_{Cone(A)}((d_{X}(x, B ∖ A)s, x), o)
= d_{X}(x, B ∖ A)s
⩽ d_{X}(x, y)s
⩽ \frac{d_{X}(x, y)}{sin (min \{\frac{π}{2}, d_{X}(x, y)\})}d_{Cone(X)}((s, x), (t, y))  (134)
⩽ πd_{Cone(X)}((s, x), (t, y)),  (135)

where (134) uses Lemma 5.1 and (135) uses (132) and the fact that the function u ↦ u/ sin u is increasing on (0, π/2]. The validity of (133) when d_{X}(x, B ∖ A), d_{X}(y, B ∖ A) > 0 follows from Lemma 5.2 with X replaced by X ∖ (\overline{B ∖ A}) and f(z) = d_{X}(z, B ∖ A) (using (132) and consequently ∥f∥_{∞} ⩽ π, combined with ∥f∥_{Lip} ⩽ 1).

Having proved (133), it follows from Lemma 5.8 that Lemma 3.16 will be proven once we show that for every s, t ∈ (0, ∞) and x, y ∈ X,

d_{Cone(A)}(𝔞(s, x), 𝔞(t, y))^{2} + d_{Cone(B)}(𝔟(s, x), 𝔟(t, y))^{2}

⩾ \frac{β^{4}}{72π^{2}} d_{Cone(X)}((s, x), (t, y))^{2}. (136)

We will actually show that

s ⩾ t and d_{X} (x, B ∖ A) ⩾ \frac{β}{2}

⟹ d_{Cone(A)}(𝔞(s, x), 𝔞(t, y))^{2} ⩾ \frac{β^{4}}{72π^{2}} d_{Cone(X)}((s, x), (t, y))^{2}. (137)

The validity of the implication (137) yields (136) since by replacing (s, x) by (t, y) if necessary we may assume without loss of generality that s ⩾ t, and (137) implies that we are done if d_{X} (x, B ∖ A) ⩾ β/2. If d_{X} (x, B ∖ A) < β/2 then note that by the triangle inequality,

d_{X}(x, B ∖ A) + d_{X}(x, A ∖ B) ⩾ d_{X}(B ∖ A, A ∖ B) ⩾^{(62)} β.

Consequently, since we are assuming that d_{X} (x, B ∖ A) < β/2, we have d_{X}(x, A ∖ B) > β/2, so an application of (137) with A replaced by B and 𝔞 replaced by 𝔟 implies that

d_{Cone(B)}(𝔟(s, x), 𝔟(t, y))^{2} ⩾ \frac{β^{4}}{18π^{2}} d_{Cone(X)}((s, x), (t, y))^{2},

yielding (136) in the remaining case.

It therefore remains to prove the implication (137). So, suppose from now on that

s ⩾ t and d_{X} (x, B ∖ A) ⩾ \frac{β}{2} .  (138)

It will be convenient to use below the following notation.

d_{x} =^{def} d_{X}(x, B ∖ A),  d_{y} =^{def} d_{X}(y, B ∖ A),  θ =^{def} d_{X}(x, y). (139)

We proceed by considering the cases d_{x} ⩾ d_{y} and d_{x} < d_{y} separately.

Case 1: d_{x} ⩾ d_{y}. In this case, since we are assuming (138) we have sd_{x} ⩾ td_{y}. Consequently,

d_{Cone(A)}(𝔞(s, x), 𝔞(t, y))

⩾ \frac{1}{3} max \{d_{x}s − d_{y}t, d_{x}s\sqrt{2(1 − cos θ)}\}  (140)

⩾ \frac{d_{x}}{3} max \{s − t, s\sqrt{2(1 − cos θ)}\}

⩾ \frac{β}{6\sqrt{2}}d_{Cone(X)}((s, x), (t, y)),  (141)

where (140) uses Lemma 5.3 and (141) uses (138) and Lemma 5.3. Since β ∈ [0, π], (141) implies the conclusion of (137).

Case 2: d_{x} < d_{y}. Fix ξ ∈ (0, 1) that will be determined later. By the convexity of the function u ↦ u^{2} we have

∀ u, v ∈ ℝ, (u − v)^{2} ⩾ ξu^{2} − \frac{ξ}{1 − ξ} v^{2}.

Consequently,

(d_{x}s − d_{y}t)^{2} = (d_{x}(s − t) + (d_{x} − d_{y})t)^{2}

⩾ ξd_{x}^{2}(s − t)^{2} − \frac{ξ}{1 − ξ} (d_{y} − d_{x})^{2}t^{2}

⩾ \frac{ξβ^{2}}{4} (s − t)^{2} − \frac{ξ}{1 − ξ} θ^{2}t^{2}  (142)
⩾ \frac{ξβ^{2}}{4} (s − t)^{2} − \frac{π^{2}ξ}{2(1 − ξ)}st(1 − cos θ),  (143)

where (142) uses the assumption d_{x} ⩾ β/2 and, by the triangle inequality, that |d_{y} − d_{x}| ⩽ d_{X}(x, y) = θ. In (143) we used (48) and the assumption s ⩾ t. Now,

d_{Cone(A)}(𝔞(s, x), 𝔞(t, y))^{2}
= (d_{x}s − d_{y}t)^{2} + 2d_{x}d_{y}st(1 − cos θ)
⩾ (d_{x}s − d_{y}t)^{2} + \frac{β^{2}}{2} st(1 − cos θ)  (144)
⩾ \frac{ξβ^{2}}{4} (s − t)^{2} + \frac{1}{4} (β^{2} − \frac{π^{2}ξ}{1 − ξ} ) · 2st(1 − cos θ)  (145)
⩾ \frac{1}{4} min \{ξβ^{2}, β^{2} − \frac{π^{2}ξ}{1 − ξ} \} · d_{Cone(X)}((s, x), (t, y))^{2},  (146)

where in (144) we used the fact that in Case 2 we are assuming that d_{y} > d_{x} ⩾ β/2 and in (145) we used (143). Choosing ξ = β^{2}/(3π^{2}) in (146) implies the conclusion of (137) in Case 2 as well. □

6. Embedding (1 + δ)-sparse graphs in L_{1}

In this section all graphs are simple. Following [ABLT06], given δ ∈ (0, 1) we say that a graph G is (1 + δ)-sparse if

∀ S ⊆ V_{G},  |E_{G}(S)| ⩽ (1 + δ)|S|,  (147)

where we recall that E_{G}(S) denotes the set of those edges in E_{G} both of whose endpoints lie in S, i.e., E_{G}(S) = {{u, v} ∈ E_{G} : u, v ∈ S}.

For t > 0 we denote the set of all cycles of G of size less than t by

ℭ_{t}(G) =^{def} {C ⊆ V : C is a cycle of G and |C| < t} .  (148)

Claim 6.1. Fix δ ∈ (0, 1) and a graph G that is (1 + δ)-sparse. Then every C ∈ ℭ_{1/δ}(G) is an induced cycle, i.e., |E_{G}(C)| = |C|.

Proof. If C is a cycle of G and |E_{G}(C)| > |C| then it follows from (147) that |C| + 1 ⩽ |E_{G}(C)| ⩽ (1 + δ)|C|, implying that |C| ⩾ 1/δ. □

The starting point of our investigations in the present section is the following result from [ALN^{+}12].

Lemma 6.2 (Lemma 3.11 of [ALN^{+}12]). Fix δ ∈ (0, 1) and a connected graph G that satisfies

∀ S ⊆ V_{G},  |E_{G}(S)| ⩽ (1 + δ)(|S| − 1).  (149)

Then

c_{1}(V_{G}, d_{G}) ≲ 1 + δ diam(G).  (150)

While the condition (149) is very close to the (1 + δ)-sparseness condition (147), it in fact implies that G also has high girth. The relation between (149) and (147) is clarified in the following lemma.

Lemma 6.3. Fix δ ∈ (0, 1) and an integer g ⩾ 3. Suppose that a graph G satisfies (149). Then G is (1 + δ)-sparse and the girth of G is at least 1+1/δ. Conversely, suppose that G is (1+δ)-sparse and the girth of G is at least g. Then

∀ S ⊆ V_{G}, |E_{G}(S)| ⩽ (1 + δ) (1 + \frac{1}{g − 1}) (|S| − 1).

Proof. If G contains a cycle of size k then k ⩽ (1+δ)(k−1) due to (149). Thus k ⩾ 1+1/δ, proving the first assertion of Lemma 6.3. Conversely, suppose that G is (1 + δ)-sparse and the girth of G is at least g. Take S ⊆ V_{G}. If |S| < g then the graph (S, E_{G}(S)) cannot contain a cycle, and it is therefore a forest. Consequently |E_{G}(S)| ⩽ |S| − 1. If |S| ⩾ g then it follows from (147) that

|E_{G}(S)| ⩽ (1 + δ)|S| ⩽ (1 + δ) (|S| − 1 + \frac{|S| − 1}{g − 1}) .  □

Lemma 6.2 therefore implicitly assumes that the graph in question has high girth, an assumption that will not be available in our context. In fact, in our setting δ will tend to 0 as |V_{G}| → ∞, so the implicit high girth assumption of Lemma 6.2 is quite restrictive. We observe that this high girth assumption is essential for the proof of Lemma 6.2 in [ALN^{+}12]. Indeed, denoting the set of all spanning trees of a connected graph G by 𝔗(G), the main step of the proof of Lemma 6.2 in [ALN^{+}12] is the following statement.

Claim 6.4 (Claim 3.14 of [ALN^{+}12]). Fix δ ∈ (0, 1) and suppose that a connected graph G satisfies the assumptions of Lemma 6.2. Then there exists a probability measure μ on 𝔗(G) such that

∀ e ∈ E_{G},  μ(\{T = (V_{G}, E_{T}) ∈ 𝔗(G) : e ∈ E_{T} \}) ⩾ \frac{1}{1 + δ} . (151)

The existence of a probability measure μ on 𝔗(G) that satisfies (151) implies that the girth of G is at least 1 + 1/δ. Indeed, if C ⊆ V_{G} is a cycle of G then since every T ∈ 𝔗(G) satisfies |E_{T} ∩ E_{G}(C)| ⩽ |C| − 1 it would follow from (151) that

|C| − 1 ⩾ ∫_{𝔗(G)} |E_{T} ∩ E_{G}(C)|dμ(T)

= ∑_{e∈E_{G}(C)} μ(\{T ∈ 𝔗(G) : e ∈ E_{T} \}) ⩾ \frac{|E_{G}(C)|}{1 + δ} ⩾ \frac{|C|}{1 + δ} ,

implying that |C| ⩾ 1 + 1/δ.

In what follows, given a connected graph G, a spanning tree T of its associated 1-dimensional simplicial complex Σ(G) is defined as follows. Given a spanning tree T_{0} = (V_{G}, E_{T_{0}}) ∈ 𝔗(G), include in T all the edges of E_{T_{0}} as unit intervals. If e = {x, y} ∈ E_{G} is an edge of G that is not an edge of T_{0} then partition the unit interval in Σ(G) that corresponds to e into two connected subsets I_{x}, I_{y}, where x ∈ I_{x} and y ∈ I_{y} (thus one of I_{x}, I_{y} is a closed interval and the other is a half open interval). T is now obtained from T_{0} by gluing I_{x} to T_{0} at the vertex x, and gluing I_{y} to T_{0} at the vertex y. Notice that the completion of T (i.e., taking the closures of the half open intervals that were glued to T_{0}) is the 1-dimensional simplicial complex of a graph theoretical tree. The set of all spanning trees of Σ(G) is denoted below by 𝔗(Σ(G)).

Here we prove the following theorem, which assumes only that the graph in question is connected and (1 + δ)-sparse.

Theorem 6.5. Fix δ ∈ (0, 1) and a (1 + δ)-sparse graph G. There exists a probability measure μ on 𝔗(Σ(G)) with respect to which every x, y ∈ Σ(G) satisfy

∫_{𝔗(Σ(G))} min {d_{T} (x, y), diam(G)} dμ(T)

≲ (1 + δ diam(G))d_{Σ(G)}(x, y). (152)

Before passing to the proof of Theorem 6.5 we state the following corollary that explains its link to Lemma 6.2.

Corollary 6.6. Under the assumptions of Theorem 6.5 we have

c_{1}(Σ(G)) ≲ 1 + δ diam(G).

Proof. By Lemma 5.4 there exists a mapping Φ : L_{1} → L_{1} such that

∀x, y ∈ L_{1}, ∥Φ(x) − Φ(y)∥_{1} ≍ min {∥x − y∥_{1}, diam(G)} .

For every T ∈ 𝔗(Σ(G)) fix an isometric embedding F_{T} : (T, d_{T}) → L_{1}. Let L_{1}(μ, L_{1}) denote the space of all mappings f : 𝔗(Σ(G)) → L_{1}, equipped with the norm

∥f∥_{L_{1}(μ,L_{1})} =^{def} ∫_{𝔗(Σ(G))} ∥f(T)∥_{1}dμ(T).

Then L_{1}(μ, L_{1}) is isometric to L_{1}. Define h : Σ(G) → L_{1}(μ, L_{1}) by

∀ T ∈ 𝔗(Σ(G)), ∀ x ∈ Σ(G), h(x)(T) = Φ(F_{T} (x)).

Then, for every x, y ∈ Σ(G),

∥h(x) − h(y)∥_{L_{1}(μ,L_{1})} ≍ ∫_{𝔗(Σ(G))} min {d_{T} (x, y), diam(G)} dμ(T). (153)

Observe that min {d_{T} (x, y), diam(G)} ≳ d_{Σ(G)}(x, y) for every spanning tree T ∈ 𝔗(Σ(G)) and x, y ∈ Σ(G), because d_{T} (x, y) ⩾ d_{Σ(G)}(x, y) and d_{Σ(G)}(x, y) ⩽ diam(G) + 1 (recall (17)). Hence, it follows from (152) and (153) that

d_{Σ(G)}(x, y) ≲ ∥h(x) − h(y)∥_{L_{1}(μ,L_{1})} ≲ (1 + δ diam(G))d_{Σ(G)}(x, y). □

Remark 6.7. Lemma 6.2 is proved in [ALN^{+}12] by first establishing the same statement as Theorem 6.5 under the stronger assumption (149), with the conclusion (152) holding for every x, y ∈ V_{G}. It is then shown in [ALN^{+}12, Claim 3.13] that for every tree T and every threshold a ∈ (0, ∞) the metric min{d_{T} , a} embeds with distortion O(1) into a convex combination of dominating tree metrics. Thus for the purpose of proving Corollary 6.6 one can use Claim 3.13 of [ALN^{+}12] rather than the stronger statement of Lemma 5.4, which deals with truncation of all of L_{1} and not just trees.

Remark 6.8. The fact that Theorem 6.5 yields an embedding of Σ(G) rather than (V_{G}, d_{G}) is needed in the ensuing arguments, but it does not add significant difficulties to the proof of Theorem 6.5.

The new contribution of Theorem 6.5 is that its conclusion holds under the assumption that G is (1 + δ)-sparse rather than under the assumption (149), i.e., without requiring that the girth of G is large. This is achieved via the following strategy: in [ALN^{+}12], given an edge {x, y} ∈ E_{G} and a spanning tree T ∈ 𝔗(G) that does not contain {x, y} as an edge, the quantity min {d_{T} (x, y), diam(G)} is bounded from above by diam(G). But by Claim 6.1, if {x, y} is an edge of a cycle C ∈ ℭ_{1/δ}(G) and |E_{T} ∩ E_{G}(C)| ⩾ |C| − 1 then d_{T} (x, y) ⩽ |C| − 1.

Our strategy is therefore to use this better bound on d_{T} (x, y), and to show that it is possible to only deal with probability measures μ on 𝔗(G) that are supported on those spanning trees T ∈ 𝔗(G) satisfying |E_{T} ∩ E_{G}(C)| ⩾ |C| − 1 for every small cycle C ∈ ℭ_{1/(3δ)}(G).

We next prove some lemmas as steps towards the proof of Theorem 6.5, starting with the following combinatorial fact whose obvious and short proof is included for completeness.

Lemma 6.9. Let G be a graph and C_{1}, C_{2} ⊆ V be distinct cycles of G such that C_{1} ∩ C_{2} ≠ ∅. Then |E_{G}(C_{1} ∪ C_{2})| ⩾ |C_{1} ∪ C_{2}| + 1.

Proof. By removing redundant edges and vertices we may assume without loss of generality that V_{G} = C_{1} ∪ C_{2} and if C_{1} = {x_{1}, …, x_{m}} and C_{2} = {y_{1}, …, y_{n}} then E_{G} = E_{1} ∪ E_{2}, where

E_{1} =^{def} {{x_{1}, x_{2}}, {x_{2}, x_{3}}, …, {x_{m−1}, x_{m}}, {x_{m}, x_{1}}},

and

E_{2} =^{def} {{y_{1}, y_{2}}, {y_{2}, y_{3}}, …, {y_{n−1}, y_{n}}, {y_{n}, y_{1}}}.

If E_{1} ∩ E_{2} = ∅ then

|E_{G}(C_{1} ∪ C_{2})| = |E| = |C_{1}| + |C_{2}| ⩾ |C_{1} ∪ C_{2}| + 1,

where the final inequality uses C_{1} ∩ C_{2} ≠ ∅. So, suppose that there exist i, j ∈ {1, …, m} and s, t ∈ {1, …, n} with |i − j| ∈ {1, m − 1}, |s − t| ∈ {1, n − 1} and x_{i} = y_{s}, x_{j} = y_{t}. If |C_{1}|, |C_{2}| > 3 then we may contract the edge {x_{i}, x_{j}} while identifying its endpoints, thus obtaining a graph G′ which is the union of two strictly smaller cycles, with |V_{G′}| = |V | − 1 and |E_{G′}| = |E| − 1. By continuing in this manner we see that it suffices to prove the desired result under the additional assumptions |C_{1}| = 3 and E_{1} ∩ E_{2} ≠ ∅. Thus either |C_{1} ∩ C_{2}| = 2 or C_{1} ⊆ C_{2}. In the former case we have

|E_{G}(C_{1} ∪ C_{2})| = |C_{2}| + 2 = |C_{1} ∪ C_{2}| + 1,

and in the latter case, since C_{1} ≠ C_{2}, we have

|E_{G}(C_{1} ∪ C_{2})| ⩾ |C_{2}| + 1 = |C_{1} ∪ C_{2}| + 1.  □

Lemma 6.10. Fix δ ∈ (0, 1) and a connected (1 + δ)-sparse graph G. Suppose that C_{1}, C_{2} ⊆ V_{G} are distinct cycles of G. Then

d_{G}(C_{1}, C_{2}) ⩾ 1 + \frac{1}{δ} − |C_{1}| − |C_{2}|.  (154)

Proof. It suffices to prove (154) under the assumption

|C_{1}| + |C_{2}| < 1 + \frac{1}{δ}.  (155)

We first note that (155) implies that C_{1} ∩C_{2} = ∅. Indeed, otherwise, using Lemma 6.9 and the fact that G is (1 + δ)-sparse, we have

|C_{1} ∪ C_{2}| + 1 ⩽ |E_{G}(C_{1} ∪ C_{2})| ⩽ (1 + δ)|C_{1} ∪ C_{2}|.

Consequently |C_{1}∪C_{2}| ⩾ 1/δ, which contradicts (155) since C_{1}∩C_{2} ≠ ∅.

Having proved that C_{1} ∩ C_{2} = ∅, we have t =^{def} d_{G}(C_{1}, C_{2}) > 0. Take x ∈ C_{1} and y ∈ C_{2} with d_{G}(x, y) = t and choose w_{0}, …, w_{t} ∈ V_{G} such that w_{0} = x, w_{t} = y and {w_{i−1}, w_{i}} ∈ E_{G} for every i ∈ {1, …, t}. By the choice of x and y as the vertices at which d_{G}(C_{1}, C_{2}) is attained, necessarily w_{1}, …, w_{t−1} ∈ V_{G} ∖ (C_{1} ∪ C_{2}). Hence,

|C_{1} ∪ C_{2} ∪ {w_{1}, …, w_{t−1}}| = |C_{1}| + |C_{2}| + t − 1,

and

|E_{G} (C_{1} ∪ C_{2} ∪ {w_{1}, …, w_{t−1}}) | ⩾ |C_{1}| + |C_{2}| + t.

Since G is (1 + δ)-sparse, it follows that

|C_{1}| + |C_{2}| + t ⩽ (1 + δ)(|C_{1}| + |C_{2}| + t − 1),

which simplifies to give d_{G}(C_{1}, C_{2}) = t ⩾ 1 + \frac{1}{δ} − |C_{1}| − |C_{2}|. □

Fixing δ ∈ (0, 1) and a connected (1 + δ)-sparse graph G, write

t =^{def} \frac{1}{3δ} ,  (156)

and define Γ ⊆ V_{G} by

Γ =^{def} ⋃_{C∈ℭ_{t}(G)} C.  (157)

We shall work below with a “quotient graph”

G/ℭ_{t}(G) = (V_{G/ℭ_{t}(G)}, E_{G/ℭ_{t}(G)}),

which is defined as follows.

V_{G/ℭ_{t}(G)} =^{def} (V_{G} ∖ Γ) ∪ ℭ_{t}(G),  (158)

i.e., the vertex set of G/ℭ_{t}(G) consists of those vertices of G that do not belong to any cycle of G of length less than t, and we append to these vertices an additional vertex for every cycle C ∈ ℭ_{t}(G). Thus

|V_{G/ℭ_{t}(G)}| = |V_{G}| − |Γ| + |ℭ_{t}(G)|.  (159)

The edges of G/ℭ_{t}(G) are defined as follows. For every x, y ∈ V_{G} ∖ Γ,

{x, y} ∈ E_{G/ℭ_{t}(G)} ⟺ {x, y} ∈ E_{G},

and for every (x, C) ∈ (V_{G} ∖ Γ) × ℭ_{t}(G),

{x, C} ∈ E_{G/ℭ_{t}(G)} ⟺ d_{G}(x, C) = 1.

Under this definition G/ℭ_{t}(G) is simple and connected. Moreover, by Lemma 6.10 every distinct C_{1}, C_{2} ∈ ℭ_{t}(G) are disjoint, and by Claim 6.1 we have E_{G}(C) = |C| for every C ∈ ℭ_{t}(G). Consequently,

|E_{G/ℭ_{t}(G)}| = |E_{G}| − ∑_{C∈ℭ_{t}(G)} |C| = |E_{G}| − |Γ|.  (160)

Lemma 6.11. For every distinct C_{1}, C_{2} ∈ ℭ_{t}(G) we have

d_{G/ℭ_{t}(G)}(C_{1}, C_{2}) > t + 1.

Proof. Suppose that d_{G/ℭ_{t}(G)}(C_{1}, C_{2}) is minimal among all distinct cycles C_{1}, C_{2} ∈ ℭ_{t}(G). Then by the definition of G/ℭ_{t}(G), the nonendpoint vertices on the shortest path joining C_{1} and C_{2} in G/ℭ_{t}(G) consist of vertices in V_{G} ∖ Γ. Consequently,

d_{G/ℭ_{t}(G)}(C_{1}, C_{2}) ⩾ d_{G}(C_{1}, C_{2}) ⩾ 1+ \frac{1}{δ} −|C_{1}|−|C_{2}| > 1+ \frac{1}{δ} −2t = t+1,

where we used Lemma 6.10, the fact that |C_{1}|, |C_{2}| < t, and (156). □

Lemma 6.12. If 0 < δ < \frac{1}{3} then

|E_{G/ℭ_{t}(G)}| ⩽ \frac{1 + δ}{1 − 3δ} (|V_{G/ℭ_{t}(G)}| − 1) .  (161)

Proof. If ℭ_{t}(G) = ∅ then by definition G = G/ℭ_{t}(G) and the girth of G is at least t. Recalling the choice of t in (156), we see that (161) holds true due to the fact that G is (1 + δ)-sparse and Lemma 6.3. We may therefore assume from now on that ℭ_{t}(G) ≠ ∅. We may also assume from now on that G/ℭ_{t}(G) contains a cycle, since otherwise it is a tree and therefore |E_{G/ℭ_{t}(G)}| ⩽ |V_{G/ℭ_{t}(G)}| − 1.

Suppose first that |ℭ_{t}(G)| = 1 and write ℭ_{t}(G) = {C}. Thus, recalling (157), we have |Γ| = |C| < t. Since G/ℭ_{t}(G) contains a cycle, it follows that |V_{G} ∖ Γ| ⩾ t/2. Indeed, if there is a cycle in G/ℭ_{t}(G) that does not contain the vertex C ∈ V_{G/ℭ_{t}(G)} then since no cycle of G other than C has length less than t, this cycle contains at least t vertices of V_{G} ∖ Γ. If on the other hand there exist distinct x_{1}, …, x_{k} ∈ V_{G} ∖ Γ such that {C, x_{1}}, {x_{1}, x_{2}}, …, {x_{k−1}, x_{k}}, {x_{k}, C} ∈ E_{G/ℭ_{t}(G)} then d_{G}(x_{1}, C) = d_{G}(x_{k}, C) = 1. By adding to {x_{1}, …, x_{k}} the vertices on the shortest path in C joining the nearest neighbors of x_{1} and x_{k} in C, we obtain a cycle of length less than k+t/2 in G that differs from C. Since we are assuming that no cycle of G other than C has length less than t, it follows that k + t/2 ⩾ t, implying that |V_{G} ∖ Γ| ⩾ k ⩾ t/2, as required. We have thus shown that

|Γ| < t ⩽ 2|V_{G} ∖ Γ|,  (162)

and consequently, since G is (1 + δ)-sparse,

|E_{G/ℭ_{t}(G)}| =^{(160)} |E_{G}| − |Γ| ⩽ (1 + δ)|V_{G}| − |Γ| = (1 + δ)|V_{G} ∖ Γ| + δ|Γ|

⩽^{(162)} (1 + 3δ)|V_{G} ∖ Γ| = (1 + 3δ) (|V_{G/ℭ_{t}(G)}| − 1) , (163)

where the last step of (163) uses (159) and the fact that we are treating the case |ℭ_{t}(G)| = 1. Since 1 + 3δ ⩽ (1 + δ)/(1 − 3δ), the desired bound (161) follows from (163).

It remains to prove (161) under the assumption |ℭ_{t}(G)| ⩾ 2. In this case, for every C ∈ ℭ_{t}(G) fix an arbitrary C′ ∈ ℭ_{t}(G) with C ≠ C′. By Lemma 6.11 if {C = v_{0}, v_{1}, …, v_{k} = C′} is the shortest path in G/ℭ_{t}(G) joining C and C′ then k > t + 1 = 1/(3δ) + 1 ⩾ 2 and v_{1}, …, v_{⌈t⌉} ∈ V ∖ Γ. If we define A(C) ⊆ V ∖ Γ by

A(C) =^{def} {v_{1}, …, v_{⌊(t+1)/2⌋}} ,

then A(C_{1}) ∩ A(C_{2}) = ∅ for every distinct C_{1}, C_{2} ∈ ℭ_{t}(G), since otherwise d_{G/ℭ_{t}(G)}(C_{1}, C_{2}) ⩽ 2⌊(t+1)/2⌋ ⩽ t+1, contradicting Lemma 6.11. This shows that

|V_{G} ∖ Γ| ⩾ | ⋃_{C∈ℭ_{t}(G)} A(C)| = ∑_{C∈ℭ_{t}(G)} |A(C)|

⩾ |ℭ_{t}(G)| · ⌊\frac{t + 1}{2}⌋ ⩾ \frac{t − 1}{2}|ℭ_{t}(G)|. (164)

Hence,

|Γ| = ∑_{C∈ℭ_{t}(G)} |C| < t|ℭ_{t}(G)|

⩽^{(164)} \frac{2t}{t − 1}|V_{G} ∖ Γ| =^{(156)} \frac{2}{1 − 3δ} |V_{G} ∖ Γ|. (165)

Now, arguing similarly to (163) we have

|E_{G/ℭ_{t}(G)}| =^{(160)} |E_{G}| − |Γ| ⩽ (1 + δ)|V_{G}| − |Γ| = (1 + δ)|V_{G} ∖ Γ| + δ|Γ|

⩽^{(165)} (1 + δ + \frac{2δ}{1 − 3δ} ) |V_{G} ∖ Γ| ⩽^{(159)} \frac{1 − 3δ^{2}}{1 − 3δ} (|V_{G/ℭ_{t}(G)}| − 2) . □

Corollary 6.13. If 0 < δ < \frac{1}{3} then the quotient graph G/ℭ_{t}(G) satisfies (149) with δ replaced by 4δ/(1 − 3δ), i.e,

∀ S ⊆ V_{G/ℭ_{t}(G)},  |E_{G/ℭ_{t}(G)}(S)| ⩽ \frac{1 + δ}{1 − 3δ} (|S| − 1).

Proof. Fix S ⊆ V_{G/ℭ_{t}(G)} and let S_{1}, …, S_{m} ⊆ S be the connected components of the graph (S, E_{G/ℭ_{t}(G)}(S)). For every i ∈ {1, …, m} we lift each S_{i} to a subset U_{i} ⊆ V_{G} that is given by

U_{i} =^{def} (S_{i} ∩ (V_{G} ∖ Γ)) ⋃ ( ⋃_{C∈S_{i}∩ℭ_{t}(G)} C) .

Since (S_{i}, E_{G/ℭ_{t}(G)}(S_{i})) is connected, the graph H_{i} =^{def} (U_{i}, E_{G}(U_{i})) is connected as well. By definition H_{i}/ℭ_{t}(H_{i}) = (S_{i}, E_{G/ℭ_{t}(G)}(S_{i})), so, since H_{i} is connected and (1 + δ)-sparse, by Lemma 6.12 we have

∀ i ∈ \{1, …, m\},  |E_{G/ℭ_{t}(G)}(S_{i})| ⩽ \frac{1 + δ}{1 − 3δ} (|S_{i}| − 1).  (166)

Consequently,

|E_{G/ℭ_{t}(G)}(S)| = ∑_{i=1}^{m} |E_{G/ℭ_{t}(G)}(S_{i})| ⩽^{(166)} \frac{1 + δ}{1 − 3δ} (∑_{i=1}^{m} |S_{i}| − m)

= \frac{1 + δ}{1 − 3δ} (|S| − m) ⩽ \frac{1 + δ}{1 − 3δ} (|S| − 1) . □

Before proceeding it will be convenient to introduce the following notation. Firstly, recalling (157), the set of edges E_{G}(Γ) ⊆ E_{G} consists of those edges in E_{G} that belong to a cycle of length less than t. Since, by Lemma 6.10, these cycles are pairwise disjoint, we can associate to every e ∈ E_{G}(Γ) a unique cycle C_{e} ∈ ℭ_{t}(G) such that e ∈ E_{G}(C_{e}). Secondly, let 𝔗_{good}(G) ⊆ 𝔗(G) be the set of those spanning trees T of G satisfying |E_{T} ∩ E_{G}(C)| = |C| − 1 for every C ∈ ℭ_{t}(G), i.e.,

𝔗_{good}(G) =^{def} {T ∈ 𝔗(G) : ∀ C ∈ ℭ_{t}(G), |E_{T} ∩ E_{G}(C)| = |C| − 1} .

Lemma 6.14. Suppose that 0 < δ < \frac{1}{3}. There exists a probability measure μ on 𝔗_{good}(G) such that for every e ∈ E_{G} ∖ E_{G}(Γ) we have

μ (\{T ∈ 𝔗_{good}(G) : e ∈ E_{T} \}) ⩾ \frac{1 − 3δ}{1 + δ} ,  (167)

and in addition for every e ∈ E_{G}(Γ) we have

μ (\{T ∈ 𝔗_{good}(G) : e ∈ E_{T} \}) ⩾ \frac{|C_{e}| − 1}{|C_{e}|}.  (168)

Proof. Due to Corollary 6.13, we can apply Claim 6.4 to the quotient graph G/ℭ_{t}(G), thus obtaining a probability measure ν on 𝔗(G/ℭ_{t}(G)) such that for every e ∈ E_{G/ℭ_{t}(G)} we have

ν(\{T ∈ 𝔗(G/ℭ_{t}(G)) : e ∈ E_{T} \}) ⩾ \frac{1 − 3δ}{1 + δ} .  (169)

By the definition of G/ℭ_{t}(G), there is a bijection between the quotient edges E_{G/ℭ_{t}(G)} and E_{G} ∖ E_{G}(Γ). Under this bijection, ν can be lifted to a probability measure σ on the subsets of E_{G} ∖ E_{G}(Γ). Let τ be the probability measure on the subsets of E_{G}(Γ) given by selecting a subset of size |C| − 1 uniformly at random from each C ∈ ℭ_{t}(Γ), where these selections are performed independently for different cycles in ℭ_{t}(G). If A ⊆ E_{G} ∖ E_{G}(Γ) and B ⊆ E_{G}(Γ) are such that σ(A), τ(B) > 0 then A ∪ B form the edges of a spanning tree of G, which by design belongs to 𝔗_{good}(G). Thus σ × τ induces a probability measure μ on 𝔗_{good}(G). The desired estimate (167) holds true due to (169). The desired estimate (168) holds true because if e ∈ E_{G}(Γ) and T ∈ 𝔗_{good}(G) is distributed according to μ then each subset of C_{e} of size |C_{e}| − 1 is contained in E_{T} with probability 1/|C_{e}|. □

Proof of Theorem 6.5. Let μ be the probability measure on 𝔗_{good}(G) from Lemma 6.14. We obtain from μ a probability distribution μ_{Σ} over spanning trees of the 1-dimensional simplicial complex Σ(G) as follows. Given T_{0} ∈ 𝔗_{good}(G) that is distributed according to μ, define T ∈ 𝔗(Σ(G)) as follows. Include all the edges of T_{0} as unit intervals in T. Let {U_{e}}_{E_{G}∖E_{T_{0}}} be i.i.d. random variables that are uniformly distributed on [0, 1], and attach to T a closed interval of length U_{e} at the vertex x and a half open interval of length 1 − U_{e} at the vertex y (here we arbitrarily choose a labeling of the endpoints of every edge in E_{G} as x and y). The distribution of the resulting spanning tree T ∈ 𝔗(Σ(G)) is denoted μ_{Σ}.

It suffices to prove (152) when x and y lie on the same interval corresponding to an edge e ∈ E_{G}. Let [x, y] denote the geodesic joining x and y in Σ(G) (it is a sub-interval of the unit interval corresponding to e). Given T ∈ 𝔗(Σ(G)), write [x, y] ⊆ T if and only if the geodesic [x, y] is also a geodesic in T. We distinguish between two cases.

Case 1. We have e ∈ E_{G} ∖ E_{G}(Γ). Then there exists a sub-interval I_{x,y} ⊆ [0, 1] of length d_{Σ(G)}(x, y) such that

μ_{Σ}( {T ∈ 𝔗(Σ(G)) : [x, y] ⊈ T } )
= μ( {T ∈ 𝔗(G) : e ∉ E_{T} } ) · Pr [U_{e} ∈ I_{x,y}]
⩽^{(167)} \frac{4δ}{1 + δ} d_{Σ(G)}(x, y).  (170)

Consequently,

∫_{𝔗(Σ(G))} min {d_{T} (x, y), diam(G)} dμ_{Σ}(T)
≲ μ_{Σ}( {T ∈ 𝔗(Σ(G)) : [x, y] ⊆ T } ) · d_{Σ(G)}(x, y)
+μ_{Σ}( {T ∈ 𝔗(Σ(G)) : [x, y] ⊈ T } ) · diam(Σ(G))
≲^{(170)} (1 + δ diam(G))d_{Σ(G)}(x, y),

which is the desired inequality (152).

Case 2. We have e ∈ E_{G}(Γ). Then arguing as in (170) with the use of (167) replaced by the use of (168),

μ_{Σ}( \{T ∈ 𝔗(Σ(G)) : [x, y] ⊈ T \} ) ⩽ \frac{d_{Σ(G)}(x, y)}{|C_{e}|}.  (171)

Moreover, if T_{0} ∈ 𝔗_{good}(G) then, since T_{0} contains all but one of the edges of C_{e}, we have d_{T} (x, y) ⩽ |C_{e}|. Since μ is supported on 𝔗_{good}(G), we conclude that

∫_{𝔗(Σ(G))} d_{T} (x, y)dμ_{Σ}(T)
⩽ μ_{Σ}( {T ∈ 𝔗(Σ(G)) : [x, y] ⊆ T } ) · d_{Σ(G)}(x, y)
+μ_{Σ}( {T ∈ 𝔗(Σ(G)) : [x, y] ⊈ T } ) · |C_{e}|
⩽^{(171)} 2d_{Σ(G)}(x, y),

proving the desired inequality (152) in Case 2 as well. □

Remark 6.15. Using Lemma 6.10 one can prove that if δ ⩽ diam(G)/15 then G can contain at most one cycle, implying that c_{1}(G) = 1.

7. Geometric properties of random regular graphs

Fix n, d ∈ ℕ. While we are interested in proving estimates about the probability distribution G_{n,d}, it is often simpler to argue about a more tractable probability distribution on 𝔾_{n}, denoted P_{n,d} and called the pairing model; see [Wor99, Sec. 2] and the references therein.

To define P_{n,d} assume from now on that nd is even and let M be a uniformly random perfect matching of the set {1, …, n} × {1, …, d} (recall that a perfect matching is a partition into subsets of size 2). Now define a random d-regular graph G with V_{G} = {1, …, n} and

∀ i, j ∈ {1, …, n}, E_{G}(i, j) =^{def} ∑_{a=1}^{d} ∑_{b=1}^{d} 1_{{{(i,a),(j,b)}∈M}}.

In other words, the edges of G are obtained by “projecting” the perfect matching M onto {1, …, n}, i.e., whenever {(i, a), (j, b)} ∈ M we add an edge joining i and j in G. The graph G that is obtained in this way from a perfect matching M of {1, …, n}×{1, …, d} is denoted G(M).

The probability measures G_{n,d} and P_{n,d} are contiguous in the following sense. As explained in [Wor99, Sec. 2.2], there exists α(d) ∈ (0, ∞) such that

∀ A ∈ 𝔾_{n}, G_{n,d}(A) ⩽ α(d) · P_{n,d}(A). (172)

By [Wor99], for d ≲ \sqrt[3]{n} we have log α(d) ≲ d^{2}, but we will not explicitly state the dependence on d from now on.

There is another standard probability distribution on n-vertex simple graphs: the Erdös-Rényi model G(n, p) for p ∈ (0, 1). The statements below on the sparsity of subsets of random graphs have been proved in [ABLT06] for the G(n, p) model, and here we need to extend them to the G_{n,d} model. This leads to some technical changes, but the essence of the argument is the same as in [ABLT06].

The following lemma is well-known, and it follows from much more precise estimates that are available in the literature (see e.g. [McK81]). We include its straightforward proof for the sake of completeness.

Lemma 7.1. Let F be a set of unordered pairs of vertices in {1, …, n} with |F | < nd/4. Then

P_{n,d} (\{G ∈ 𝔾_{n} : F ⊆ E_{G}\}) ⩽ (\frac{2d}{n})^{|F |} .  (173)

Proof. Write k = |F | and F = {f_{1}, …, f_{k}}. Let M be a uniformly random matching of {1, …, n} × {1, …, d}. For every ℓ ∈ {1, …, k} write f_{ℓ} = {i_{ℓ}, j_{ℓ}}.

We claim that for every ℓ ∈ {1, …, k} we have

Pr [f_{ℓ} ∈ G(M)|f_{1}, …, f_{ℓ−1} ∈ G(M)] ⩽ \frac{2d}{n},  (174)

where Pr is the uniform probability on the matching M. Once (174) is proved, the desired estimate (173) would follow because by the definition of P_{n,d} we have

P_{n,d} ({G ∈ 𝔾_{n} : F ⊆ E_{G}})

= ∏_{ℓ=1}^{k} Pr [f_{ℓ} ∈ G(M)|f_{1}, …, f_{ℓ−1} ∈ G(M)] ⩽^{(174)} (\frac{2d}{n})^{k} .

To verify (174) observe that in order for {i_{ℓ}, j_{ℓ}} to be in E_{G(M)} there must be a, b ∈ {1, …, d} such that {(i_{ℓ}, a), (j_{ℓ}, b)} ∈ M. For every fixed a ∈ {1, …, d}, if we know that f_{1}, …, f_{ℓ−1} ∈ G(M) then there are nd − 2(ℓ − 1) − 1 possible elements of {1, …, n} × {1, …, d} that can be matched by M to (i_{ℓ}, a). We are assuming that ℓ ⩽ k < nd/4, so that nd − 2(ℓ − 1) − 1 ⩾ nd/2. We therefore have at least nd/2 pairs in {1, …, n} × {1, …, d} that can be matched to (i_{ℓ}, a), from which at most d can project to the edge f_{ℓ}. The probability for this to happen is therefore at most 2/n. The desired estimate (174) now follows since there are at most d possible values of a. □

Definition 7.2. For every ε, δ ∈ (0, ∞) denote by S_{ε,δ} the set of all graphs G with the property that |E_{G}(S)| < (1+δ)|S| for every S ⊆ V_{G} satisfying |S| ⩽ |V_{G}|^{1−ε}.

Lemma 7.3 below is similar to [ABLT06, Lem 2.8], which treats the same question for Erdös-Rényi graphs (see also [ALN^{+}12, Lem. 3.10]).

Lemma 7.3. For ε ∈ (0, 1) and two integers n, d ⩾ 3 define

δ =^{def} \frac{7 log d}{ε log n}.  (175)

Then

1 − G_{n,d} (S_{ε,δ}) ≲_{d,ε} \frac{1}{n^{1−ε}} .  (176)

Proof. By adjusting the implicit constant in (176) if necessary, we may assume in the computations below that n is larger than an appropriate constant that may depend only on ε and d. In particular, we assume throughout that n > d^{7/ε}, or equivalently that δ ∈ (0, 1).

For every k ∈ {3, …, n} define B_{δ}^{k} to be the set of all graphs G for which there exists S ⊆ V_{G} with |S| = k and |E_{G}(S)| ⩾ (1 + δ)k. The complement of the event S_{ε,δ} is the union of B_{δ}^{k} for k ∈ {3, …, ⌊n^{1−ε}⌋}. Therefore, due to (172) we have

1 − G_{n,d} (S_{ε,δ}) ≲_{d} 1 − P_{n,d} (S_{ε,δ}) ⩽ ∑_{k=3}^{⌊n^{1−ε}⌋} P_{n,d} (B_{δ}^{k}) .  (177)

Since our assumption n > d^{7/ε} implies that k ⩽ nd/16, by Lemma 7.1,

P_{n,d} (B_{δ}^{k}) ⩽ \binom{n}{k}\binom{\binom{k}{2}}{⌈(1 + δ)k⌉} (\frac{2d}{n})^{⌈(1+δ)k⌉}

⩽ (\frac{en}{k})^{k} (\frac{ek(k − 1)d}{n⌈(1 + δ)k⌉})^{⌈(1+δ)k⌉}  (178)

⩽ (\frac{en}{k} (\frac{e(k − 1)d}{(1 + δ)n})^{1+δ})^{k} ⩽ (\frac{e^{3}k^{δ}d^{1+δ}}{n^{δ}} )^{k} .  (179)

In (178) we used the standard estimate \binom{m}{ℓ} ⩽ (\frac{em}{ℓ})^{ℓ}, which holds for every m ∈ ℕ and ℓ ∈ {1, …, m}. In the penultimate inequality of (179) we used the fact that the function x ↦ (ek(k−1)d/(nx))^{x} is decreasing when x ⩾ dk(k − 1)/n, and ⌈(1 + δ)k⌉ ⩾ (1 + δ)k ⩾ dk(k − 1)/n by our assumption n > d^{7/ε}.

If k < 1/δ then ⌈(1 + δ)k⌉ = k + 1, and therefore (178) implies that P_{n,d} (B_{δ}^{k}) ≲ k(e^{2}d)^{k+1}/n. Using this estimate in combination with (179) for k ⩾ 1/δ, it follows from (177) that

1 − G_{n,d} (S_{ε,δ}) ≲_{d} \frac{1}{n} ∫_{3}^{1+1/δ} x(e^{2}d)^{x+1}dx + ∫_{1/δ}^{n^{1−ε}+1} (\frac{e^{3}x^{δ}d^{1+δ}}{n^{δ}} )^{x} dx

≲_{d} \frac{(e^{2}d)^{1/δ}}{nδ} + ∫_{1/δ}^{2n^{1−ε}} (\frac{e^{3}x^{δ}d^{1+δ}}{n^{δ}} )^{x} dx.  (180)

To estimate the final integral in (180), observe first that due to (175), its integrand is less than 1, i.e, e^{3}2^{δ}n^{(1−ε)δ}d^{1+δ}/n^{δ} < 1. Since δ ⩽ 1 and n^{εδ} = d^{7}, this would follow from d^{7} > 2e^{3}d^{2}, which is true since d ⩾ 3. Now fix M ∈ [1/δ, 2n^{1−ε}] and proceed as follows.

∫_{1/δ}^{2n^{1−ε}} (\frac{e^{3}x^{δ}d^{1+δ}}{n^{δ}} )^{x} dx

⩽ ∫_{1/δ}^{M} (\frac{e^{3}M^{δ}d^{1+δ}}{n^{δ}} )^{x} dx + ∫_{M}^{2n^{1−ε}} (\frac{e^{3}(2n^{(1−ε)})^{δ}d^{1+δ}}{n^{δ}} )^{x} dx

⩽ ∫_{1/δ}^{∞} (\frac{e^{3}M^{δ}d^{1+δ}}{n^{δ}} )^{x} dx + ∫_{M}^{∞} (\frac{2^{δ}e^{3}d^{1+δ}}{n^{εδ}} )^{x} dx

= \begin{matrix}(e^{3}M^{δ}d^{1+δ}n^{−δ}) & 1/δ \\ log & (n^{δ}e^{−3}M^{−δ}d^{−1−δ}) \end{matrix} + \begin{matrix}(2^{δ}e^{3}d^{1+δ}n^{−εδ}) & M \\ log & (n^{εδ}2^{−δ}e^{−3}d^{−1−δ}) \end{matrix}

≲_{d} \frac{M(e^{3}d)^{1/δ}}{n} + \frac{1}{d^{M}} ,  (181)

where in (181) we used the fact that n^{εδ} = d^{7} and d ⩾ 3. Choosing M = log n and substituting (181) into (180) while using (175) and the fact that e^{3}d ⩽ d^{4} (because d ⩾ 3), we conclude that

1 − G_{n,d} (S_{ε,δ}) ≲_{d} \frac{d^{4/δ} log n}{n} =^{(175)} \frac{n^{4ε/7} log n}{n} ≲_{ε} \frac{1}{n^{1−ε}} .  □

The final preparatory lemma that we will need about graphs sampled from G_{n,d} is that they have only a few short cycles. Specifically, Lemma 7.4 below asserts that the probability that a graph sampled from G_{n,d} has less than \sqrt{n} cycles of length ⌈(log_{d−1} n)/7⌉ is at least 1 − c(d)/\sqrt[3]{n}, where c(d) ∈ (0, ∞) may depend only on d. This is a standard fact but we include its simple proof here since we could not locate the statement below in the literature. Precise asymptotics of the expected numbers of short cycles of a graph sampled from G_{n,d} were obtained in [MWW04], and Lemma 7.4 follows from the estimates of [MWW04] by Markov’s inequality.

Lemma 7.4. For every two integers n, d ⩾ 3 we have

G_{n,d} (\{G ∈ 𝔾_{n} : |ℭ_{⌈(log_{d−1} n)/7⌉}(G)| ⩾ \sqrt{n}\}) ≲_{d} \frac{1}{\sqrt[3]{n}}.

(Recall that the set of cycles of length less than t ∈ ℕ in a graph G was denoted in (148) by ℭ_{t}(G).)

Proof. For r ∈ ℕ write X_{r}(G) =^{def} |ℭ_{r+1}(G)| − |ℭ_{r}(G)|, i.e., X_{r}(G) is the number of cycles of length r in G. By [MWW04, Eq. (2.2)], for r ⩽ (log_{d−1} n)/7 we have the expectation bound ∫_{𝔾_{n}} X_{r}dG_{n,d} ≲ (d−1)^{r}. Hence by Markov’s inequality, for every integer r ⩽ (log_{d−1} n)/7,

G_{n,d} (\{G ∈ 𝔾_{n} : X_{r}(G) ⩾ \frac{\sqrt{n}}{⌈(log_{d−1} n)/7⌉}\})

≲ \frac{(d − 1)^{⌈(log_{d−1} n)/7⌉} log_{d−1} n}{\sqrt{n}} ≲_{d} \frac{log n}{n^{5/14}}.

Consequently,

G_{n,d} (\{G ∈ 𝔾_{n} : |ℭ_{⌈(log_{d−1} n)/7⌉}(G)| ⩾ \sqrt{n}\})

= G_{n,d} (\{G ∈ 𝔾_{n} : ∑_{r=3}^{⌈(log_{d−1} n)/7⌉} X_{r}(G) ⩾ \sqrt{n}\})

⩽ ∑_{r=3}^{⌈(log_{d−1} n)/7⌉} G_{n,d} (\{G ∈ 𝔾_{n} : X_{r}(G) ⩾ \frac{\sqrt{n}}{⌈(log_{d−1} n)/7⌉}\})

≲_{d} \frac{(log n)^{2}}{n^{5/14}} ≲ \frac{1}{\sqrt[3]{n}}.  □

7.1. Proof of Lemma 3.12. By lemma 7.3 we know that if G is sampled from G_{n,d} then with high probability for every S ⊆ V_{G} with |S| ⩽ n^{1−ε} the graph (S, E_{G}(S)) is (1 + δ)-sparse. One is then tempted to use Corollary 6.6 in order to embed the metric space (S, d_{G}) into L_{1}, but this is problematic since the metric that the graph (S, E_{G}(S)) induces on S can be very different from the restriction of d_{G} to S (in fact, (S, E_{G}(S)) may not even be connected). Following an idea of [ALN^{+}12], the following lemma will be used to remedy this matter.

Lemma 7.5. Fix n, d, r ∈ ℕ and let G be a connected n-vertex graph whose maximum degree is d. Suppose that S, T ⊆ V_{G} satisfy

S ⊆ ⋃_{u∈T} B_{G}(u, r),  (182)

where B_{G}(u, r) =^{def} {v ∈ V_{G} : d_{G}(u, v) ⩽ r} denotes the ball of radius r and center u in the metric space (V_{G}, d_{G}). Then there exists U ⊆ V_{G} with S ⊆ U such that

|U| ⩽ |T | (d(d − 1)^{3r−1} + diam(G)) ,  (183)

and if we let H denote the graph (U, E_{G}(U)) then

diam(H) ⩽ 6r + 2 diam(G),

and for every a, b ∈ S,

d_{G}(a, b) ⩽ d_{H}(a, b) ⩽ 2 (\frac{diam(G)}{r} + 1) d_{G}(a, b).  (184)

Proof. For every x, y ∈ V_{G} let P_{x,y} ⊆ V_{G} be an arbitrary shortest path joining x and y in G. Fix z ∈ V_{G} and define

U =^{def} ( ⋃_{x∈T} B_{G}(x, 3r)) ⋃ ( ⋃_{x∈T} P_{x,z}) .

Then,

|U| ⩽ ∑_{x∈T} |B_{G}(x, 3r)| + ∑_{x∈T} |P_{x,z}|
⩽ |T |d(d − 1)^{3r−1} + ∑_{x∈T}(d_{G}(x, z) − 1) + 1  (185)
⩽ |T |d(d − 1)^{3r−1} + |T |(diam(G) − 1) + 1,

where in (185) we used the fact that since G has maximum degree d, for every k ∈ ℕ and w ∈ G we have |B_{G}(w, k)| ⩽ d(d − 1)^{k−1}. The fact that diam(H) ⩽ 6r + 2 diam(G) is immediate from the definition of U.

Having proved (183), we proceed to prove (184). Because H is a subgraph of G, the leftmost inequality in (184) holds true for every a, b ∈ V_{G}. So, fixing a, b ∈ S, it remains to prove the rightmost inequality in (184). To do so we distinguish between two cases.

Case 1. There exists x ∈ T such that a ∈ B_{G}(x, r) and b ∈ B_{G}(x, 2r). Then d_{G}(a, b) ⩽ d_{G}(a, x) + d_{G}(b, x) ⩽ 3r. Thus |P_{a,b}| ⩽ 3r + 1. Write P_{a,b} = {w_{0} = a, w_{1}, …, w_{m} = b} with m ⩽ 3r and {w_{i−1}, w_{i}} ∈ E_{G} for every i ∈ {1, …, m}. For every i ∈ {0, …, 2r} we have d_{G}(w_{i}, a) ⩽ 2r, and therefore d_{G}(w_{i}, x) ⩽ d_{G}(w_{i}, a)+d_{G}(x, a) ⩽ 3r. Similarly, for every i ∈ {2r+1, …, m} we have d_{G}(w_{i}, b) ⩽ r, and therefore d_{G}(w_{i}, x) ⩽ 3r. This shows that P_{a,b} ⊆ B_{G}(x, 3r) ⊆ U, and hence d_{G}(a, b) = d_{H}(a, b).

Case 2. There exist distinct x, y ∈ T such that a ∈ B_{G}(x, r) and b ∈ B_{G}(y, r) ∖ B_{G}(x, 2r). Then

d_{G}(a, b) ⩾ d_{G}(x, b) − d_{G}(x, a) ⩾ r.  (186)

Since a ∈ B_{G}(x, r) we have P_{a,x} ⊆ B_{G}(x, r) ⊆ U. For the same reason, P_{b,y} ⊆ U. Also, by the definition of U we have P_{x,z}, P_{y,z} ⊆ U. By considering the path P_{a,x} ∪ P_{x,z} ∪ P_{y,z} ∪ P_{b,y} ⊆ U we see that

d_{H}(a, b) ⩽ d_{G}(a, x) + d_{G}(x, z) + d_{G}(y, z) + d_{G}(b, y)

⩽ 2r + 2 diam(G) ⩽^{(186)} 2 (1 + \frac{diam(G)}{r}) d_{G}(a, b),

completing the verification of (184) in this case as well.

Note that due to (182) one of the above two cases must occur, so the proof of (184) is complete. □

Definition 7.6. Fix two integers d, t ⩾ 3 and a d-regular simple graph G. For every cycle C of G fix an arbitrary edge e_{C} ∈ E_{G}(C). Denote

I_{G}^{t} =^{def} {e_{C} : C ∈ ℭ_{t}(G)} .

Thus I_{G}^{t} contains a single representative edges from each cycle of G of length less than t (formally, I_{G} depends also on the choices of the edges e_{C}, but we fix such a choice once and for all and do not indicate this dependence explicitly). Denote the graph (V_{G}, E_{G} ∖ I_{G}^{t}) by L_{G}^{t}. Note that by definition we have girth(L_{G}^{t}) ⩾ t.

Before proceeding we record for future use two simple lemmas about the concepts that were introduced in Definition 7.6.

Lemma 7.7. Fix three integers r, t, d ⩾ 3 and write

η =^{def} \frac{1}{r + 2t − 3}.  (187)

Suppose that G is a connected d-regular graph such that for every subset S ⊆ V_{G} we have

|S| ⩽ d(d − 1)^{\frac{t+r}{2} −1} · |ℭ_{t}(G)| ⟹ |E_{G}(S)| ⩽ (1 + η)|S|.  (188)

Then for every distinct cycles C_{1}, C_{2} ∈ ℭ_{t}(G) we have d_{G}(C_{1}, C_{2}) ⩾ r. Moreover, the graph L_{G}^{t} is connected and

diam (L_{G}^{t}) ⩽ \frac{t + r − 1}{r + 1} · diam(G) + \frac{r(t − 2)}{r + 1}.  (189)

Proof. Define a set of edges F ⊆ E_{G} by

F =^{def} ⋃_{e∈I_{G}^{t}} \{f ∈ E_{G} : d_{G}(e, f) ⩽ ⌊\frac{t − 1}{2}⌋ + ⌊\frac{r − 1}{2}⌋\} .  (190)

Also, let U =^{def} ⋃_{f∈F} f ⊆ V_{G} be the set of vertices that belong to an edge in F. Let H be the graph (U, F). For every C ∈ ℭ_{t}(G) choose an arbitrary vertex v_{C} ∈ e_{C}. By the definition of F, for every u ∈ U there exists C ∈ ℭ_{t}(G) such that

d_{G}(u, v_{C}) ⩽ 1 + ⌊\frac{t − 1}{2}⌋ + ⌊\frac{r − 1}{2}⌋ ⩽ \frac{t + r}{2}.

Consequently,

|U| ⩽ ∑_{C∈ℭ_{t}(G)} |B_{G} (v_{C}, \frac{t + r}{2})| ⩽ d(d − 1)^{\frac{t+r}{2} −1}|ℭ_{t}(G)|.

By our assumption (188), the graph H is (1+η)-sparse. An application of Lemma 6.10 to the connected components of H shows that if C_{1} and C_{2} are distinct cycles of H of length less than t then either they are contained in different connected components of H or

d_{H}(C_{1}, C_{2}) ⩾ 1 + \frac{1}{η} − 2(t − 1) =^{(187)} r.  (191)

It follows from (191) that any distinct cycles C_{1}, C_{2} ∈ ℭ_{t}(G) also satisfy d_{G}(C_{1}, C_{2}) ⩾ r. Indeed, observe first that for every C ∈ ℭ_{t}(G) we have C ⊆ U. This is true because the diameter (in the metric d_{G}) of C is at most ⌊(|C| − 1)/2⌋ ⩽ ⌊(t − 1)/2⌋, and therefore by (190) we have E_{G}(C) ⊆ F. This also shows that ℭ_{t}(G) = ℭ_{t}(H). Now suppose for the sake of obtaining a contradiction that C_{1}, C_{2} ∈ ℭ_{t}(G) and 0 < d_{G}(C_{1}, C_{2}) ⩽ r −1. Then there exist u_{1}, …, u_{r} ∈ V_{G} such that u_{1} ∈ C_{1}, u_{r} ∈ C_{2} and {u_{i−1}, u_{i}} ∈ E_{G} for every i ∈ {2, …, r}. We have d_{G}(u_{1}, e_{C_{1}}) ⩽ ⌊(t − 1)/2⌋ and d_{G}(u_{r}, e_{C_{2}}) ⩽ ⌊(t − 1)/2⌋. This implies that d_{G}(u_{i}, C_{1} ∪C_{2}) ⩽ ⌊(t−1)/2⌋+⌊(r −1)/2⌋ for every i ∈ {1, …, r}, and therefore by (190) we have {u_{i−1}, u_{i}} ∈ F for every i ∈ {2, …, r}, implying that d_{H}(C_{1}, C_{2}) ⩽ r − 1, in contradiction to (191).

To prove (189), take u, v ∈ V_{G} and write d_{G}(u, v) = m ⩽ diam(G). Let {w_{0} = w, w_{1}, …, w_{m} = v} ⊆ V_{G} be distinct vertices such that {w_{i−1}, w_{i}} ∈ E_{G} for every i ∈ {1, …, m}. We proceed to examine the edges on this path that are not in E_{L_{G}^{t}}. Thus, suppose that j_{1}, …, j_{k} ∈ {0, …, m − 1} are such that for every ℓ ∈ {1, …, k} we have {w_{j_{ℓ}}, w_{j_{ℓ}+1}} = e_{C_{ℓ}} for some C_{ℓ} ∈ ℭ_{t}(G), and {w_{i−1}, w_{i}} ≠ e_{C} for every i ∈ {1, …, m} ∖ {j_{1}, …, j_{k}} and every C ∈ ℭ_{t}(G). Since we proved that distinct cycles in ℭ_{t}(G) are at distance at least r (in the metric d_{G}), it follows that j_{ℓ+1} − j_{ℓ} ⩾ r + 1 for every ℓ ∈ {1, …, k − 1}. Hence m ⩾ (k − 1)(r + 1) + 1, or equivalently k ⩽ (m + r)/(r + 1). Now, for every ℓ ∈ {1, …, k} replace each edge {w_{j_{ℓ}}, w_{j_{ℓ}+1}} by the path C_{ℓ} ∖ {e_{C_{ℓ}}}, whose length is at most t − 2. By the definition of L_{G}^{t}, we thus obtain a new path joining u and v, all of whose edges are edges of L_{G}^{t}, and its length is at most m + k(t − 2). By substituting the above upper bound on k we conclude that

d_{H}(u, v) ⩽ m + \frac{(m + r)(t − 2)}{r + 1} ⩽ \frac{t + r − 1}{r + 1} · diam(G) + \frac{r(t − 2)}{r + 1}. □

Lemma 7.8. Fix three integers M, t, d ⩾ 3 and suppose that G is a connected d-regular simple graph such that d_{G}(C_{1}, C_{2}) > 8M for every distinct C_{1}, C_{2} ∈ ℭ_{t}(G). Suppose also that

min_{\begin{matrix}S⊆G \\ 0<|S|⩽n/2 \end{matrix}} \frac{E_{G}(S, V_{G} ∖ S)}{|S|} ⩾ \frac{1}{M} .  (192)

Then

min_{\begin{matrix}S⊆G \\ 0<|S|⩽n/2 \end{matrix}} \frac{E_{L_{G}^{t}}(S, V_{G} ∖ S)}{|S|} ⩾ \frac{1}{4M} .

Proof. Fix S ⊆ V_{G} with 0 < |S| ⩽ |V_{G}|/2. Write

𝒞 =^{def} {C ∈ ℭ_{t}(G) : e_{C} ∩ S ≠ ∅},

and for every C ∈ 𝒞 choose an arbitrary endpoint u_{C} ∈ e_{C} ∩ S. Define

T =^{def} {u_{C}}_{C∈𝒞} ⊆ V_{G}.

If |S| ⩾ 2M|T | then

\frac{E_{L_{G}^{t}}(S, V_{G} ∖ S)}{|S|} ⩾ \frac{E_{G}(S, V_{G} ∖ S) − |T |}{|S|} ⩾^{(192)} \frac{1}{2M} .

So, we may assume from now on that |S| < 2M|T |. Because every distinct x, y ∈ T satisfy d_{G}(x, y) > 8M, the balls {B_{G}(x, 4M)}_{x∈T} are disjoint. This implies that if we define

W =^{def} {x ∈ T : |B_{G} (x, 4M) ∩ S| < 4M} ,

then |W | > |T |/2, since otherwise |S| ⩾ 4M|T ∖ W | ⩾ 2M|T |.

For every x ∈ W choose a maximal k(x) ∈ ℕ ∪ {0} for which there exists w_{1}(x), …, w_{k}(x) ∈ S such that

{x, w_{1}(x)}, {w_{1}(x), w_{2}(x)}, …, {w_{k−1}(x), w_{k}(x)} ∈ E_{L_{G}^{t}} = E_{G} ∖ I_{G}^{t}.

In other words, we are considering a maximal path starting from x in the graph (S, E_{L_{G}^{t}}(S)). Since |B_{G} (x, 4M) ∩ S| < 4M it follows that k(x) ⩽ 4M − 1. Since w_{k(x)}(x) belongs to at most one edge in I_{G}^{t}, and w_{k(x)}(x) has d ⩾ 3 neighbors in G, it follows that there exists z(x) ∈ V_{G} ∖ {w_{k(x)−1}(x)} such that {w_{k(x)}, z(x)} ∈ E_{L_{G}^{t}}. By the maximality of k(x) we necessarily have {w_{k(x)}, z(x)} ∈ E_{L_{G}^{t}}(S, V_{G} ∖S). Moreover, for distinct x, y ∈ W we have {w_{k(x)}, z(x)} ≠ {w_{k(y)}, z(y)}, since otherwise, because d_{G}(x, w_{k(x)}(x)), d_{G}(y, w_{k(y)}(x)) ⩽ 4M − 1 and d_{G}(x, z(x)), d_{G}(y, z(y)) ⩽ 4M, it would follow that d_{G}(x, y) ⩽ 8M.

We have proved that E_{L_{G}^{t}}(S, V_{G} ∖ S) ⩾ |W | > |T |/2 > |S|/(4M), completing the proof of Lemma 7.8. □

Proof of Lemma 3.12. Fix a parameter M ∈ (1, ∞) that will be determined later. Let E_{M}^{1} denote the family of all connected n-vertex d-regular simple graphs G that satisfy the following conditions

diam(G) ⩽ M log_{d} n,  (193)

and

min_{\begin{matrix}S⊆G \\ 0<|S|⩽n/2 \end{matrix}} \frac{E_{G}(S, V_{G} ∖ S)}{|S|} ⩾ \frac{1}{M} .

By the proofs in [BFdlV82] and [Bol88] we can fix M to be a sufficiently large universal constant such that

1 − G_{n,d} (E_{M}^{1}) ≲_{d} \frac{1}{n}.  (194)

(The papers [BFdlV82, Bol88] contain much more precise information on the diameter and expansion of random regular graphs, respectively.)

By adjusting the value of the constant C(d) in Lemma 3.12, it suffices to prove the required statement under the assumption n ⩾ d^{2000M}. Define δ ∈ (0, 1) and an integer t ⩾ 3 by

δ =^{def} \frac{21}{log_{d} n},  and t =^{def} ⌊\frac{log_{d} n}{63}⌋ .  (195)

Also, set from now on ε = 1/3. Let E^{2} denote the family of graphs S_{ε,δ} (recall Definition 7.2) and let E^{3} denote the family of all connected n-vertex d-regular simple graphs G that satisfy |ℭ_{t}(G)| ⩽ \sqrt{n}. By Lemma 7.3 and Lemma 7.4 we have

1 − G_{n,d} (E^{2}) ≲_{d} \frac{1}{n^{2/3}}  and 1 − G_{n,d} (E^{3}) ≲_{d} \frac{1}{\sqrt[3]{n}}  (196)

By virtue of (194) and (196), if we denote ℒ =^{def} E_{M}^{1} ∩ E^{2} ∩ ℰ^{3} then

1 − G_{n,d} (ℒ) ≲_{d} \frac{1}{\sqrt[3]{n}}.

We will now show that for a large enough universal constant K ∈ (0, ∞) the graph family ℒ can serve as the graph family ℒ_{K}^{n,d} of Lemma 3.12.

Take G ∈ ℒ and write I = I_{G}^{t} and L = L_{G}^{t}. By the definition of E^{3} we have |I| ⩽ \sqrt{n}, as required. By the definition of S_{ε,δ}, for every S ⊆ V_{G} with |S| ⩽ n^{2/3} we have |E_{G}(S)| ⩽ (1+δ)|S|. Since n ⩾ d^{200}, by the definitions (195) we have n^{2/3} ⩾ d(d − 1)^{t−1}\sqrt{n} and δ ⩽ 1/(3t − 3). Since |ℭ_{t}(G)| ⩽ \sqrt{n}, this implies that assumption (188) of Lemma 7.7 is satisfied, with r = t. It therefore follows from Lemma 7.7 that d_{G}(C_{1}, C_{2}) ⩾ t for distinct C_{1}, C_{2} ∈ ℭ_{t}(G) and

diam(L) ⩽^{(189)} \frac{2t − 1}{t + 1} diam(G) + \frac{t(t − 2)}{t + 1} ⩽^{(193)∧(195)} 3M log_{d} n. (197)

This shows that assertion (b) of Definition 3.11 holds true provided K ⩾ 3M. Since by (195) and the assumption n ⩾ d^{2000M} we have t > 8M, we may also use Lemma 7.8 to deduce that

min_{\begin{matrix}S⊆G \\ 0<|S|⩽n/2 \end{matrix}} \frac{E_{L}(S, V_{G} ∖ S)}{|S|} ⩾ \frac{1}{4M} .

Hence, assertion (c) of Definition 3.11 holds true provided K ⩾ 4M. By the definition of L we have

girth(L) ⩾ t ⩾^{(195)∧(197)} \frac{diam(G)}{378M}.  (198)

So, assertion (1) of Definition 3.10 holds true provided K ⩾ 378M.

It remains to prove that if K is large enough then the graph L satisfies assertion (2) of Definition 3.10. Suppose that S_{0} ⊆ Σ(V_{G}) satisfies |S_{0}| ⩽ \sqrt{n}. Let S ⊆ V_{G} denote the union of S_{0} ∩ V_{G} with the endpoints of all the edges in E_{L} whose corresponding unit interval in the one-dimensional simplicial complex Σ(L) contains a point from S_{0}∖V_{G}. Thus |S| ⩽ 2\sqrt{n}. Apply Lemma 7.5 with S = T and r = ⌊(log_{d} n)/36⌋. We obtain U ⊆ V_{G} with U ⊇ S such that if we let H denote the graph (U, E_{L}(U)) then

diam(H) ⩽ 6r + 2 diam(L) ⩽^{(197)} 4M log_{d} n,  (199)

and for every a, b ∈ S

d_{L}(a, b) ⩽ d_{H}(a, b) ⩽ 2 (\frac{diam(L)}{r} + 1) d_{L}(a, b)

⩽^{(197)} 2 (\frac{3M log_{d} n}{⌊(log_{d} n)/36⌋} + 1) d_{L}(a, b) ≲ Md_{L}(a, b). (200)

Since U ⊇ S, and S contains S_{0} ∩V_{G} and the endpoints of all the edges of Σ(L) that contain points in S_{0} ∖ V_{G}, it follows from (200) that

∀ x, y ∈ S_{0},  d_{Σ(L)}(x, y) ⩽ d_{Σ(H)}(x, y) ≲ Md_{Σ(L)}(x, y). (201)

Also, by (183), and using the fact that n ⩾ d^{2000M}, we have

|U| ⩽ 2\sqrt{n} (n^{1/12} + diam(L)) ⩽^{(197)} 2n^{7/12} + 2M\sqrt{n} · log_{d} n ⩽ n^{2/3}.

Since G belongs to S_{ε,δ}, it follows that H is (1 + δ)-sparse. By Corollary 6.6 we conclude that

c_{1} (Σ(H)) ≲ 1 + δ diam(H) ⩽^{(195)∧(199)} 1 + 84M.

Due to (201) we therefore have

c_{1} (S_{0}, \frac{2π}{girth(L)} · d_{Σ(L)}) ≲ M,

and by Corollary 3.8,

c_{1} (Cone (S_{0}, \frac{2π}{girth(L)} · d_{Σ(L)})) ≲ M.

This concludes the proof of assertion (2) of Definition 3.10 provided K is a sufficiently large multiple of (the universal constant) M. □

7.2. Proof of Proposition 3.15. We shall continue using here the notation and assumptions that were used in the proof of Lemma 3.12. Suppose that G ∈ ℒ. We have already seen that if G is distributed according to G_{n,d} then this happens with probability at least 1−a(d)/\sqrt[3]{n} for some a(d) ∈ (0, ∞).

Recalling the definition of t in (195), define A_{1}, A_{2} ⊆ Σ(G) as follows.

A_{1} =^{def} ⋃_{e∈I_{G}^{t}} {x ∈ Σ(G) : d_{Σ(G)}(x, e) ⩽ t} ,

and

A_{2} =^{def} Σ(G) ∖ ⋃_{e∈I_{G}^{t}} \{x ∈ Σ(G) : d_{Σ(G)}(x, e) ⩽ \frac{t}{2}\} .

Then A_{1} ∪ A_{2} = Σ(G) and

d_{Σ(G)} (A_{1} ∖ A_{2}, A_{2} ∖ A_{1}) ⩾ \frac{t}{2} ≳^{(195)} log_{d} n.  (202)

As in the proof of Lemma 3.12, denote I =^{def} I_{G}^{t} and L =^{def} L_{G}^{t}. We also let Φ ⊆ Σ(G) be the union of all the unit intervals corresponding to the edges in I. Thus A_{1} is the t-neighborhood of Φ in Σ(G) and A_{2} is the complements of the (t/2)-neighborhood of Φ in Σ(G).

We claim that

∀ x, y ∈ A_{2},  d_{Σ(G)}(x, y) ⩽ d_{Σ(L)}(x, y) ⩽ 3d_{Σ(G)}(x, y).  (203)

Since E_{L} ⊆ E_{G}, only the rightmost inequality in (203) requires proof. Fix x, y ∈ A_{2} and let P_{x,y} : [0, d_{Σ(G)}(x, y)] → Σ(G) be a geodesic joining x and y in Σ(G). If P_{x,y} ⊆ Σ(L) then d_{Σ(G)}(x, y) = d_{Σ(L)}(x, y). The situation is therefore only interesting when P_{x,y} ∩ Φ ≠ ∅. In this case, since the distances of x and y from Φ are at least t/2, the length of P_{x,y} must be at least t. Let J_{1}, …, J_{k} ⊆ [0, d_{Σ(G)}(x, y)] be a maximal collection of intervals such that P_{x,y}(J_{i}) ∈ I for every i ∈ {1, …, k}. We have shown in the proof of Lemma 3.12 that (using Lemma 7.7) we have d_{Σ(G)}(P_{x,y}(J_{i}), P_{x,y}(J_{j})) ⩾ t for every distinct i, j ∈ {1, …, k}. This means that the length of P_{x,y} is at least (k − 1)t, or equivalently that k ⩽ 1+d_{Σ(G)}(x, y)/t. Each edge P_{x,y}(J_{i}) lies on a cycle C_{i} ∈ ℭ_{t}(G), and all the other edges of C_{i} are in E_{L}. Therefore, if we replace each edge P_{x,y}(J_{i}) by the path C_{i} ∖ P_{x,y}(J_{i}) (whose length is at most t − 1), we will obtain a path joining x and y in Σ(H) of length at most

d_{Σ(G)}(x, y) + k(t − 1) ⩽ d_{Σ(G)}(x, y) + (1 + \frac{d_{Σ(G)}(x, y)}{t}) (t − 1)

⩽ 2d_{Σ(X)}(x, y) + t ⩽ 3d_{Σ(X)}(x, y),

where we used the fact that d_{Σ(G)}(x, y) ⩾ t. This completes the verification of (203).

The scaling factor σ of Proposition 3.15 will be chosen to be

σ =^{def} \frac{2π}{girth(L)} ≍^{(197)∧(198)} \frac{1}{log_{d} n}.  (204)

with this choice, due to (202) assertion (II) of Proposition 3.15 holds true. We have already proved in Lemma 3.12 that L ∈ F_{K}, and therefore Cone(Σ(L), σd_{Σ(L)}) is isometric to a subset of 𝒳_{K}. By (203) and Fact 3.5 we therefore have

c_{𝒳_{K}} (Cone(A_{2}, σd_{Σ(G)})) ⩽ c_{Cone(Σ(L),σd_{Σ(L)})} (Cone(A_{2}, σd_{Σ(G)})) ⩽ 3,

thus proving assertion (IV) of Proposition 3.15.

To prove assertion (V) of Proposition 3.15 we define an embedding f : Σ(G) → Cone (Σ(G)) by f(x) = (1/\sqrt{2}, x). For every x, y ∈ Σ(G),

d_{Cone(Σ(G),σd_{Σ(G)})}(f(x), f(y)) =^{(43)} \sqrt{1 − cos (min\{π, σd_{Σ(G)}(x, y)\})}

≍^{(48)∧(204)} min \{π, \frac{d_{Σ(G)}(x, y)}{log_{d} n}\} ≍^{(193)} d_{Σ(G)}(x, y).

All that remains is to prove assertion (III) of Proposition 3.15. Define S = A_{1}∩V_{G} and T = Φ∩V_{G} = ⋃_{e∈I} e. Thus |T | ⩽ 2|I| ⩽ 2\sqrt{n}. By the definition of A_{1}, condition (182) of Lemma 7.5 holds true with r = t. Consequently, by Lemma 7.5 there exists U ⊆ V_{G} with U ⊇ S such that if we let H denote the graph (U, E_{G}(U)) then for every a, b ∈ S,

d_{G}(a, b) ⩽ d_{H}(a, b) ⩽ 2 (\frac{diam(G)}{t} + 1) d_{G}(a, b) ≲^{(193)∧(195)} d_{G}(a, b).

Since U ⊇ S and S = A_{1} ∩ V_{G}, it follows that

∀ x, y ∈ A_{2},  d_{Σ(G)}(x, y) ≍ d_{Σ(H)}(x, y).  (205)

Moreover, using the assumption n ⩾ d^{2000M}, it follows from (183) that

|U| ⩽ |T | (d^{3t} + diam(G)) ⩽^{(193)∧(195)} 2\sqrt{n} (n^{1/21} + M log_{d} n) ⩽ n^{2/3}.

recalling that G belongs to S_{ε,δ}, we conclude that H is (1 + δ)-sparse, and therefore by Corollary 6.6,

c_{1} (A_{2}, d_{Σ(G)}) ≲^{(205)} c_{1} (Σ(H), d_{Σ(H)}) ≲ 1 + δ diam(H) ≲^{(195)∧(199)} 1.

Now assertion (III) of Proposition 3.15 follows by Corollary 3.8. □

8. Are two stochastically independent random graphs expanders with respect to each other?

Here we present partial progress towards (a positive solution of) Question 2.4. Proposition 8.1 below is based on ideas of U. Feige.

Proposition 8.1. For n ∈ ℕ even let G, H be two i.i.d. random graphs sampled from G_{n,3}. Then with probability that tends to 1 as n → ∞, for every permutation π ∈ S_{n} we have

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(π(i), π(j))^{2} ≲ \frac{1}{n} ∑_{\{i,j\}∈E_{G}} d_{H}(π(i), π(j))^{2}.  (206)

A positive answer to Question 2.4 would require proving (206) when G, H are independent random 3-regular graphs of possibly different cardinalities, say G sampled from G_{n,3} and H sampled from G_{m,3}, and with the permutation π of Proposition 8.1 replaced with a general mapping f : {1, …, n} → {1, …, m}. Note that for fixed m ∈ ℕ we have γ_{+}(G, d_{H}^{2}) ≲ (log m)^{2} asymptotically almost surely. Indeed, G is asymptotically almost surely an expander, and by Bourgain’s embedding theorem [Bou85], the metric space ({1, …, m}, d_{H}) embeds into ℓ_{2} with bi-Lipschitz distortion O(log m). When m > n one can show that asymptotically almost surely

γ_{+}(G, d_{H})^{2} ≲ max \{1, (\frac{log n}{log(m/n)})^{2}\} .  (207)

The validity of (207) follows from the fact that G is asymptotically almost surely an expander, combined with an application of Lemma 7.3 and Corollary 6.6 to the random graph H.

Below we shall use the following version of Chernoff’s bound (see e.g [AS08, Thm. A.1.12]). Suppose that X_{1}, …, X_{n} are i.i.d. random variables taking values in {0, 1} and write Pr[X_{i} = 1] = p. Then for every β ∈ (1, ∞) we have

Pr [∑_{i=1}^{n} X_{i} > βpn] < (\frac{e^{β−1}}{β^{β}} )^{pn} .  (208)

Proof of Proposition 8.1. By [BFdlV82] with probability that tends to 1 as n → ∞ we have diam(H) ≲ log n. The left hand side of (206) therefore asymptotically almost surely satisfies

\frac{1}{n^{2}} ∑_{i=1}^{n} ∑_{j=1}^{n} d_{H}(π(i), π(j))^{2} ≲ (log n)^{2}.

Since there are n! possible permutations π ∈ S_{n}, it suffices to prove that there exists a universal constant c ∈ (0, ∞) such that for every fixed permutation π ∈ S_{n} we have

G_{n,3} × G_{n,3} (\{G, H ∈ 𝔾_{n} : \frac{1}{n} ∑_{\{i,j\}∈E_{G}} d_{H}(π(i), π(j))^{2} ⩾ c(log n)^{2}\})

⩾ 1 − \frac{o(1)}{n!}. (209)

Because the distribution of the metric d_{H} is invariant under permutations, it suffices to prove (209) when π is the identity permutation.

To this end, given a 3-regular graph H ∈ 𝔾_{n}, consider the following subset of the unordered pairs of elements of {1, …, n}.

N_{H} =^{def} \{\{i, j\} ∈ \binom{\{1, …, n\}}{2} : d_{H}(i, j) ⩽ \frac{log n}{16}\} .

Since H is 3-regular we have |N_{H}| ⩽ \frac{3}{2}n^{17/16}.

In order to prove (209) (with c = 1/16 and π the identity mapping) it suffice to prove that for every fixed 3-regular graph H ∈ 𝔾_{n} we have

G_{n,3} (\{G ∈ 𝔾_{n} : |E_{G} ∩ N_{H}| > \frac{4n}{3}\}) ⩽ \frac{o(1)}{n!}.  (210)

By (172) it suffices to prove (210) in the pairing model, i.e.,

P_{n,3} (\{G ∈ 𝔾_{n} : |E_{G} ∩ N_{H}| > \frac{4n}{3}\}) ⩽ \frac{o(1)}{n!}.  (211)

Assume from now on that n is divisible by 4. The proof for general even n follows mutatis mutandis from the argument below, the only difference being that one needs to round certain numbers to their nearest integers.

Recall that in the pairing model P_{n,3} we choose a matching M of P = {1, …, n} × {1, 2, 3} uniformly at random, and “project it” onto {1, …, n} so as to get a random 3-regular graph G(M). It will be convenient to order the \binom{3n}{2} pairs in \binom{P}{2} arbitrarily. Once this is done, the uniformly random matching M can be obtained by choosing its edges sequentially, where at each step we choose an additional edge uniformly at random from the unused pairs. Fix a 3-regular graph H ∈ 𝔾_{n} and let Y_{i}^{H} be the {0, 1}-valued random variable that takes the value 1 if and only if ith edge is in N_{H}. Note that Y_{i}^{H} is obtained by flipping a biased coin with success probability p_{i} which is itself a random variable depending on the first i − 1 edges of M. However, for i ⩽ 5n/4 we have

p_{i} ⩽ \frac{|N_{H}|}{\binom{3n−2(i−1)}{2}} ⩽ \frac{2|N_{H}|}{(3n − 2i)^{2}} ⩽ \frac{8|N_{H}|}{n^{2}} ⩽ \frac{12n^{17/16}}{n^{2}} = \frac{12}{n^{15/16}} =^{def} p.

We proceed by a standard coupling argument. Having sampled Y_{1}^{H}, …, Y_{5n/4}^{H}, we sample a sequence X_{1}, …, X_{5n/4} of {0, 1}-valued random variables as follows. If Y_{i}^{H} = 1 then X_{i} = 1, and if Y_{i}^{H} = 0 then X_{i} is obtained by tossing an independent coin with success probability (p − p_{i})/(1 − p_{i}). Then Pr[X_{i} = 1] = p, and because the coins that we used are independent, X_{1}, …, X_{5n/4} are independent random variables. Moreover, we have the point-wise inequality X_{i} ⩾ Y_{i}^{H} for every i ⩽ 5n/4. Now,

P_{n,3} (\{G ∈ 𝔾_{n} : |E_{G} ∩ N_{H}| > \frac{4n}{3}\}) = Pr [∑_{i=1}^{3n/2} Y_{i}^{H} > \frac{4n}{3}]

⩽ Pr [∑_{i=1}^{5n/4} Y_{i}^{H} > \frac{13n}{12}] ⩽ Pr [∑_{i=1}^{5n/4} X_{i}^{H} > \frac{13n}{12}] .

Hence by (208) with β = \frac{13n^{15/16}}{180} , we obtain the bound

P_{n,3} (\{G ∈ 𝔾_{n} : |E_{G} ∩ N_{H}| > \frac{4n}{3}\}) ⩽ (\frac{180e}{13})^{\frac{13n}{12}} n^{− \frac{65}{64} n} = \frac{o(1)}{n!},

implying the desired estimate (211). □

Acknowledgements. We are grateful to Uriel Feige, Konstantin Makarychev and Yuval Peres for helpful discussions, and to Jon Kleinberg for formulating Question 2.4. We also thank Takefumi Kondo for sending us a preliminary version of [Kon12]. M. M. was supported by ISF grants 221/07 and 93/11, BSF grant 2010021, and NSF grants CCF-0832797 and DMS-0835373. Part of this work was completed while M. M. was visitor at Microsoft Research and a member of the Institute for Advanced Study at Princeton. A. N. was supported by NSF grant CCF-0832795, BSF grant 2010021, the Packard Foundation and the Simons Foundation. Part of this work was completed while A. N. was a Visiting Fellow at Princeton University.

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