Detecting the Coarse Geometry of Networks


Clustering and sampling are key methods for the study of relational data. Learning efficient representations of such data relies on the identification of major geometric and topological features and therefore a characterization of its coarse geometry. Here, we introduce an efficient sampling method for identifying crucial structural features using a discrete notion of Ricci curvature. The introduced approach gives rise to a complexity reduction tools that allows for reducing large relational structures (eg, networks) to a concise core structure on which to focus further, computationally expensive analysis and hypothesis testing.

In NeurIPS, Relational Representation Learning.