Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks

TL;DR

This paper introduces Global Interactive Pattern (GIP) learning, a novel interpretable framework for graph classification that captures global interactions and long-range dependencies, improving interpretability and maintaining competitive accuracy.

Contribution

It proposes a new intrinsically interpretable scheme using global interactive patterns and clustering to explain graph-level decisions, addressing limitations of subgraph explanations.

Findings

GIP achieves superior interpretability over existing methods.

GIP maintains competitive performance on benchmark datasets.

Extensive experiments validate the effectiveness of GIP in real-world scenarios.

Abstract

Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studies have embraced the subgraph-specific viewpoint that attributes the decision results to the salient features and local structures of nodes. However, graph-level tasks necessitate long-range dependencies and global interactions for advanced GNNs, deviating significantly from subgraph-specific explanations. To bridge this gap, this paper proposes a novel intrinsically interpretable scheme for graph classification, termed as Global Interactive Pattern (GIP) learning, which introduces learnable global interactive patterns to explicitly interpret decisions. GIP first tackles the complexity of interpretation by clustering numerous nodes using a constrained graph clustering module. Then, it matches the coarsened global interactive instance with a batch of self-interpretable graph prototypes, thereby facilitating a transparent graph-level reasoning process. Extensive experiments conducted on both synthetic and real-world benchmarks demonstrate that the proposed GIP yields significantly superior interpretability and competitive performance to~the state-of-the-art counterparts. Our code will be made publicly available.