K-plex Cover Pooling for Graph Neural Networks

Davide Bacciu, Alessio Conte, Roberto Grossi, Francesco Landolfi, Andrea Marino

Keywords: Graph Neural Networks, Graph Pooling, Graph Coarsening, K-plexes, Vertex Cover

TL;DR: We propose a non-parametric pooling algorithm for Graph Neural Networks based on k-plexes

Abstract:

We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern.
Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links.
The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs.