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AbstractWe focus on the problem of predicting social media user's future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structuresdoi:10.1007/s43926-021-00018-3 fatcat:f7srl4pb2vd43cty32wcydbpzy