Explanations for Graph Neural Networks using A Game-theoretic Value

Graph Neural Networks (GNNs) have achieved remarkable performance on various learning tasks on geometric data. However, the incorporation of graph structures into the learning of node representations makes them challenging to understand. The core of many existing methods is to find essential subgraphs as explanations via perturbing the input graph. Typically, these methods focus on how to extract the subgraphs and the design of the scoring functions. In order to obtain a more accurate explanation and better obtain information from the graph structure, in this paper, we first propose our goal of providing a subgraph explanation for GNNs for node classification tasks. Then, we introduce MGExplainer, a post-hoc local model agnosticism explanation method designed explicitly for GNNs. Specifically, MGExplainer gives a node importance score calculated from a structure-aware Hamiach-Navarro (HN) value of Game theory, which aims to use the graph structure better. For the subgraph extraction strategy, as it is more difficult to calculate the exact HN value on larger graphs, we propose a central node sampling strategy based on Monte Carlo sampling combined with the shortest path to complete the node score calculation. Finally, we present the explanation of the subgraph in terms of the restriction score. Experiments on real-world and synthetic datasets show that MGExplainer achieves state-of-the-art performance compared to baseline models.