Work

Semantic and relation aware neural network model for bi-class multi-relational heterogeneous graphs

Year: 2025

Type: article

Abstract: 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.

Source: iScience

DOI: 10.1016/j.isci.2025.112155

Authors: Yufei Zhao, Hua Liu, Hua Duan

Institution: Shandong University of Science and Technology

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,240

Funders: National Natural Science Foundation of China, Taishan Scholar Project of Shandong Province, National Key Research and Development Program of China, National Key Research and Development Program of China Stem Cell and Translational Research