Reinforced Structural Reasoning for Receptive Field Optimization in GNN toward Interpretable Graph Clustering

Abstract

Graph clustering aims to group nodes into meaningful clusters, a crucial task for understanding the behavior of complex networks and their underlying structural organization. Leveraging the expressive power of deep learning, graph neural networks (GNNs) have emerged as popular tools for performing graph clustering with high accuracy. However, despite their promise, existing GNN-based approaches often lack structural reasoning mechanisms to adaptively infer and optimize each node's receptive field in a task-specific manner, limiting their potential to further improve both accuracy and interpretability. In this paper, we propose a novel reinforced structural reasoning framework, termed RGIGC, for interpretable graph clustering. RGIGC formulates receptive field configuration as a structural reasoning problem and employs reinforcement learning with two Q-learning agents to infer and optimize the structural receptive field of each node. These agents are guided by reward criteria that strategically shape the clustering process to achieve desired outcomes. By identifying critical structures of receptive fields that capture the intrinsic features of nodes, RGIGC can enhance the interpretability of the resulting clusters. Extensive experiments on eight real-world graph datasets of varying scales have been conducted, and the results demonstrate that RGIGC outperforms state-of-the-art graph clustering algorithms in both accuracy and interpretability.