GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs

TL;DR

GISExplainer introduces a game-theoretic approach to generate connected, causal subgraph explanations for GNNs, improving interpretability by considering both positive and negative interactions within coalitions.

Contribution

It proposes a novel causal attribution mechanism and a sequential decision process for explaining GNN predictions, addressing limitations of existing perturbation-based methods.

Findings

Outperforms state-of-the-art methods in fidelity and sparsity

Produces connected, human-interpretable explanatory subgraphs

Effectively captures both positive and negative coalition effects

Abstract

Explainability is crucial for the application of black-box Graph Neural Networks (GNNs) in critical fields such as healthcare, finance, cybersecurity, and more. Various feature attribution methods, especially the perturbation-based methods, have been proposed to indicate how much each node/edge contributes to the model predictions. However, these methods fail to generate connected explanatory subgraphs that consider the causal interaction between edges within different coalition scales, which will result in unfaithful explanations. In our study, we propose GISExplainer, a novel game-theoretic interaction based explanation method that uncovers what the underlying GNNs have learned for node classification by discovering human-interpretable causal explanatory subgraphs. First, GISExplainer defines a causal attribution mechanism that considers the game-theoretic interaction of multi-granularity coalitions in candidate explanatory subgraph to quantify the causal effect of an edge on the prediction. Second, GISExplainer assumes that the coalitions with negative effects on the predictions are also significant for model interpretation, and the contribution of the computation graph stems from the combined influence of both positive and negative interactions within the coalitions. Then, GISExplainer regards the explanation task as a sequential decision process, in which a salient edges is successively selected and connected to the previously selected subgraph based on its causal effect to form an explanatory subgraph, ultimately striving for better explanations. Additionally, an efficiency optimization scheme is proposed for the causal attribution mechanism through coalition sampling. Extensive experiments demonstrate that GISExplainer achieves better performance than state-of-the-art approaches w.r.t. two quantitative metrics: Fidelity and Sparsity.