Semantic and relation aware neural network model for bi-class multi-relational heterogeneous graphs
Yufei Zhao, Hua Liu, Hua Duan · iScience · 2025
This paper constructs three bi-class multi-relational heterogeneous graphs based on real-world data, and it proposes a semantic and relation aware neural network model (SRA-BMHN) designed for bi-class multi-relational heterogeneous graphs. SRA-BMHN consists of two core modules: first, the semantic-aware module, which integrates diverse relational semantic information using a non-linear mapping function and attention mechanism tailored to specific relationship semantics; second, the relation-aware module, which employs a hierarchical bipartite subgraph aggregation strategy. This module first divides the bi-class multi-relational heterogeneous graphs into multiple bipartite subgraphs based on different edge relationship types and captures the topological information within them. It then merges these subgraphs into a relation-weighted subgraph and captures the relational features of nodes on the weighted heterogeneous subgraph. Finally, SRA-BMHN fuses the features from the semantic-aware module and the relation-aware module to generate the final node embeddings. Experimental results on three datasets demonstrate the superiority of the proposed method.