Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling

Heterogeneous graph neural networks have gained great popularity in tackling various network analysis tasks on heterogeneous network data. However, most existing works mainly focus on general heterogeneous networks, and assume that there is only one type of edge between two nodes, while ignoring the multiplex characteristics between multi-typed nodes in multiplex heterogeneous networks and the different importance of multiplex structures among nodes for node embedding. In addition, the over-smoothing issue of graph neural networks limits existing models to only capturing local structure signals but hardly learning the global relevant information of the network. To tackle these challenges, this work proposes a model called Behavior Pattern based Heterogeneous Graph Neural Network (BPHGNN) for multiplex heterogeneous network embedding. Specifically, BPHGNN can collaboratively learn node representations across different multiplex structures among nodes with adaptive importance learning from local and global perspectives in multiplex heterogeneous networks through depth behavior pattern aggregation and breadth behavior pattern aggregation. Extensive experiments on six real-world networks with various network analytical tasks demonstrate the significant superiority of BPHGNN against state-of-the-art approaches in terms of various evaluation metrics.