Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
This addresses the need for interpretable GNNs in graph-level tasks, offering a novel method for global pattern learning rather than incremental improvements.
The paper tackles the problem of interpreting graph-level decisions in Graph Neural Networks (GNNs) by proposing Global Interactive Pattern (GIP) learning, which introduces learnable global interactive patterns to explicitly interpret decisions, resulting in significantly superior interpretability and competitive performance compared to state-of-the-art methods.
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.