DynHEN: A heterogeneous network model for dynamic bipartite graph representation learning

Abstract

Learning node representations in graphs is a widespread problem in node classification and link prediction. Current research has focused on static heterogeneous, homogeneous networks, and dynamic homogeneous networks. However, many existing graphs, such as citation and social networks, are heterogeneous. Therefore, it is still a great challenge to obtain heterogeneous information in a dynamic heterogeneous graph to assist node representation learning. In this paper, we propose a Dynamic HEterogeneous Network (DynHEN) for user-item bipartite networks. It is a discrete dynamic graph neural network model that can be used directly for node representation learning by utilizing dynamic heterogeneous graphs. Specifically, DynHEN takes a bipartite graph at each time step as input, gets the corresponding embedding by capturing the deep heterogeneous information of the nodes while fusing the temporal information. To illustrate the effectiveness of heterogeneous information for graph representation learning, we compare with the current SOTA methods in the two type experiments of link prediction and node classification, and achieve outstanding results.