Self-Interpretable Subgraph Neural Network with Deep Reinforcement Walk Exploration

Abstract

Graph neural networks (GNNs) face dual challenges of limited structural expressiveness and opaque decision-making processes. Recent research on Subgraph Neural Networks (SGNNs) enhance model expressiveness through subgraph ensembles. However, their reliance on predefined sampling strategies leads to poor interpretability and computational inefficiency. Meanwhile, post-hoc GNN explainers enhance model interpretability but still struggle to translate their explanations into model improvements. This paper presents a novel framework that fundamentally bridges this gap by developing SGNNs with intrinsic interpretability. Our key innovation lies in constructing a self-interpretable architecture where the explanation generation mechanism is organically integrated with the prediction process. Our proposed Self-Interpretable SGNN introduces a reinforcement walk exploration (RWE-SGNN) as its data-driven sampling strategy, which can dynamically extract discriminative substructures during model training. This reinforcement walk exploration module not only provides inherent interpretability, but also enables: (1) efficient substructure extraction with less candidate number and simper embedding than traditional subgraph generation methods; and (2) provable equivalence in node coverage to traditional subgraph generation methods for connected subgraphs. Experiments on graph classification tasks show accuracy improvements over state-of-the-art GNNs, with case studies validating that the automatically identified subgraphs align with domain-specific knowledge.