While graph neural networks (GNNs) have achieved notable success in various mining tasks, conventional GNNs only model the pairwise correlation 1-hop neighbors without considering long-term relations and high-order patterns, thus limiting their performances. Recently, several works addressed these issues by exploring motif, i.e., frequent subgraphs. However, methods usually require an unaccepta...