A granular approach for enhancing node representation in heterogeneous graph learning

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.