EXPLAINING TEMPORAL GRAPH MODELS THROUGH AN EXPLORER-NAVIGATOR FRAMEWORK

While Graph Neural Network (GNN) explanation has recently received significant attention, existing works are generally designed for static graphs. Due to the prevalence of temporal graphs, many temporal graph models have been proposed, but explaining their predictions still remains to be explored. To bridge the gap, in this paper, we propose a Temporal GNN Explainer (T-GNNExplainer) method. Specifically, we regard a temporal graph as a sequence of temporal events between nodes. Given a temporal prediction of a model, our task is to find a subset of historical events that lead to the prediction. To handle this combinatorial optimization problem, T-GNNExplainer includes an explorer to find the event subsets with Monte Carlo Tree Search (MCTS), and a navigator that learns the correlations between events and helps reduce the search space. In particular, the navigator is trained in advance and then integrated with the explorer to speed up searching and achieve better results. To the best of our knowledge, T-GNNExplainer is the first explainer tailored for temporal graph models. We conduct extensive experiments to evaluate the performance of T-GNNExplainer. Experimental results demonstrate that T-GNNExplainer can achieve superior performance with up to ~50% improvement in Area under Fidelity-Sparsity Curve.