HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion - Yufei Zhao; Hua Liu; Hua Duan (IEEE Access)
Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in heterogeneous graphs (HGs). However, existing methods face critical challenges, including the loss of graph attribute information caused by excessive emphasis on semantic information and the difficulty of effectively integrating graph attributes with semantic information. To address these issues, this paper proposes HGNN-GAMS: a Heterogeneous Graph Neural Network for Graph Attribute Mining and Semantic Fusion. The model comprises two main components: graph attribute fusion and semantic aggregation. The graph attribute fusion module captures two intrinsic features of HGs—their unique topological structures and node attributes. The semantic aggregation module, leveraging an attention mechanism, integrates diverse semantic information within HGs. Ultimately, HGNN-GAMS fuses graph attribute features and semantic features to produce the final feature representation. This work pioneers the integration of graph attributes with semantic information and validates the model’s effectiveness through extensive experiments on real-world datasets.