Learnt Sparsification for Interpretable Graph Neural Networks
This addresses the challenge of making GNNs more interpretable for researchers and practitioners in fields relying on relational data, though it is an incremental improvement as it builds on existing GNN frameworks.
The paper tackles the problem of interpretability in Graph Neural Networks (GNNs) by proposing Kedge, a method for explicit graph sparsification that removes unnecessary neighbors, resulting in pruning over 80% of edges on the PubMed dataset with only a 2% accuracy drop.
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.