Multi-graph aggregated graph neural network for heterogeneous graph representation learning

Heterogeneous graph neural networks have attracted considerable attention for their proficiency in handling intricate heterogeneous structures. However, most existing methods model semantic relationships in heterogeneous graphs by manually defining meta-paths, inadvertently overlooking the inherent incompleteness of such graphs. To address this issue, we propose a multi-graph aggregated graph neural network (MGAGNN) for heterogeneous graph representation learning, which simultaneously leverages attribute similarity and high-order semantic information between nodes. Specifically, MGAGNN first employs the feature graph generator to generate a feature graph for completing the original graph structure. A semantic graph is then generated using a semantic graph generator, capturing higher-order semantic information through automatic meta-path learning. Finally, we aggregate the two candidate graphs to reconstruct a new heterogeneous graph and learn node embedding by graph convolutional networks. Extensive experiments on real-world datasets demonstrate the superior performance of the proposed method over state-of-the-art approaches.