  We consider the explanation problem of Graph Neural Networks (GNNs). Most
existing GNN explanation methods identify the most important edges or nodes but
fail to consider substructures, which are more important for graph data. The
only method that considers subgraphs tries to search all possible subgraphs and
identify the most significant subgraphs. However, the subgraphs identified may
not be recurrent or statistically important. In this work, we propose a novel
method, known as MotifExplainer, to explain GNNs by identifying important
motifs, recurrent and statistically significant patterns in graphs. Our
proposed motif-based methods can provide better human-understandable
explanations than methods based on nodes, edges, and regular subgraphs. Given
an input graph and a pre-trained GNN model, our method first extracts motifs in
the graph using well-designed motif extraction rules. Then we generate motif
embedding by feeding motifs into the pre-trained GNN. Finally, we employ an
attention-based method to identify the most influential motifs as explanations
for the final prediction results. The empirical studies on both synthetic and
real-world datasets demonstrate the effectiveness of our method.
