Multi-view heterogeneous graph representation learning with fusion of high-order information and low-order information

Dengdi Sun; Yundong Meng; Bin Luo; Zhuanlian Ding

doi:10.1117/12.3033607

13 June 2024 Multi-view heterogeneous graph representation learning with fusion of high-order information and low-order information

Dengdi Sun, Yundong Meng, Bin Luo, Zhuanlian Ding

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318073 (2024) https://doi.org/10.1117/12.3033607
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Graph neural networks (GNNs) are extensively employed in the analysis of data structured as graphs and excel at learning intricate node relationships. However, traditional GNN methods encounter difficulties when applied to analyzing heterogeneous graphs (HGs) with diverse node types and relationships, due to the complex heterogeneity and semantics of the data. Therefore, we propose Multi-view Heterogeneous Graph Representation Learning with Fusion of High-Order Information and Low-Order Information(MHGRL). MHGRL produces node embeddings by integrating node content transformation, meta-path-based intra- and inter-aggregation, as well as the amalgamation of low- and high-order information from multiple perspectives. Comprehensive experiments on three diverse heterogeneous graph datasets reveal the significant benefits of our MHGRL in node classification endeavors.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dengdi Sun, Yundong Meng, Bin Luo, and Zhuanlian Ding "Multi-view heterogeneous graph representation learning with fusion of high-order information and low-order information", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318073 (13 June 2024); https://doi.org/10.1117/12.3033607

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