Cooperative Explanations of Graph Neural Networks

Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua

Abstract: With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations.In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, <u>C</u>ooperative <u>G</u>NN <u>E</u>xplanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.