Heterogeneous graph neural network fusing multi-semantic view encoding

Abstract:

Heterogeneous graph neural networks have been extensively applied in data mining, information retrieval, and related domains. The metapath-based approach captures composite relationships in heterogeneous graphs by aggregating metapath neighborhood information. However, the selection of metapaths predominantly relies on prior knowledge, which may lead to the loss or overwriting of semantic information. Additionally, the use of attention mechanisms in feature aggregation incurs substantial computational overhead, and semantic confusion may arise as the network deepens or metapaths lengthen. To address these issues, a heterogeneous graph neural network that integrates multi-semantic view encoding is proposed. Firstly, all metapaths of fixed length are selected for the target node type, and subgraphs are constructed to extract corresponding semantic information. A lightweight mean aggregator is employed to obtain node representation under different metapath subgraphs, and specific relation encodings are learned for each type of metapath to combine with node representation. Subsequently, feature mapping is carried out and node features from different semantic views are fused to derive the final representation, which is applied to downstream tasks. Experiments conducted on five real-world datasets demonstrate that the proposed model more effectively captures semantic information in heterogeneous graphs, enhances node representation performance, and outperforms mainstream baseline models in node classification and link prediction tasks in most cases. The effectiveness of the model is further validated through ablation studies and parameter sensitivity analyses.