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Multi-graph aggregated graph neural network for heterogeneous graph representation learning
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DOI：10.1007/s13042-024-02294-1.png)
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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.
Keyword:
Graph neural network
Heterogeneous graph
Multi-graph aggregation
Graph representation learning
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