Title: Rethinking the Explanation of Graph Neural Network via Non-parametric Subgraph Matching. Abstract: The great success in graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant to the prediction?'' However, current approaches usually resort to a black-box to decipher another black-box (i.e., GNN), making it difficult to understand how the explanation is made. Based on the observation that graphs typically share some joint motif patterns, we propose a novel subgraph matching framework named MatchExplainer to explore explanatory subgraphs. 
It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance between them. After that, an external graph ranking is followed to select the most informative substructure from all subgraph candidates. Thus, MatchExplainer is entirely non-parametric. 
Moreover, present graph sampling or node dropping methods usually suffer from the false positive sampling problem. To ameliorate that issue, we take advantage of MatchExplainer to fix the most informative portion of the graph and merely operate graph augmentations on the rest less informative part, which is dubbed as MatchDrop. 
We conduct extensive experiments on both synthetic and real-world datasets, showing the effectiveness of our MatchExplainer by outperforming all parametric baselines with large margins. Additional results also demonstrate that our MatchDrop is a general paradigm to be equipped with GNNs for enhanced performance.