Work

A granular approach for enhancing node representation in heterogeneous graph learning

Year: 2025

Type: article

Abstract: Heterogeneous graph learning aims to generate meaningful node representations for graph-structured data with diverse node types and complex relations, facilitating downstream tasks such as node classification and clustering. However, existing methods often emphasize either coarse-grained relational structures or fine-grained node attributes, paying limited attention to the other, which constrains their ability to fully capture the intricate interplay between nodes and relations. To address this limitation, we propose a novel Granular Interaction Heterogeneous Graph Auto-Encoder (GIHGAE), which effectively balances granular fusion and interactions in heterogeneous graph learning. Specifically, GIHGAE employs a relation-level encoder as the primary structure extractor to capture coarse-grained relational dependencies across the graph. Complementarily, we design a node-level encoder that integrates fine-grained contextual details from diverse node attributes, refining representations. These multi-granular features are fused into holistic node embeddings. Additionally, to ensure seamless integration of fine-grained and coarse-grained information, we introduce a global-level decoder to model interactions between nodes and relations explicitly. Finally, to further enhance GIHGAE, we incorporate a dual-loss mechanism, combining reconstruction loss for feature preservation and prediction loss to enhance downstream task performance. Extensive experimental evaluations in heterogeneous graph learning tasks highlight the strong performance of GIHGAE, which consistently outperforms current state-of-the-art methods in classification accuracy, clustering quality, and link prediction performance.

Source: Future Generation Computer Systems

DOI: 10.1016/j.future.2025.108080

Authors: Ying Sun, Hongjiang Ye, Feiyi Xu, Zhenjiang Dong, Yanfei Sun, Jin Qi

Institutions: Nanjing University of Posts and Telecommunications, Wuxi University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,000

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Nanjing University of Posts and Telecommunications