Learnt Sparsification for Interpretable Graph Neural Networks

TL;DR

This paper introduces Kedge, a method for explicitly sparsifying graphs in GNNs using a tractable distribution, improving interpretability and countering over-smoothing with minimal accuracy loss.

Contribution

Kedge provides a modular, end-to-end trainable approach for graph sparsification that enhances interpretability and deep GNN performance.

Findings

Kedge can remove over 80% of edges with only 2% accuracy loss.

Graph structure contributes less than node features in PubMed.

Kedge mitigates over-smoothing in deep GNNs.

Abstract

Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as inductive biases resulting in flexible and powerful models. However, GNNs remain hard to interpret as the interplay between node features and graph structure is only implicitly learned. In this paper, we propose a novel method called Kedge for explicitly sparsifying the underlying graph by removing unnecessary neighbors. Our key idea is based on a tractable method for sparsification using the Hard Kumaraswamy distribution that can be used in conjugation with any GNN model. Kedge learns edge masks in a modular fashion trained with any GNN allowing for gradient based optimization in an end-to-end fashion. We demonstrate through extensive experiments that our model Kedge can prune a large proportion of the edges with only a minor effect on the test accuracy. Specifically, in the PubMed dataset, Kedge learns to drop more than 80% of the edges with an accuracy drop of merely 2% showing that graph structure has only a small contribution in comparison to node features. Finally, we also show that Kedge effectively counters the over-smoothing phenomena in deep GNNs by maintaining good task performance with increasing GNN layers.