Dual-branch subgraph-pattern attention graph neural network for link prediction

Zhongxuan Ouyang; Jinhua Wang

doi:10.1117/12.3049976

8 November 2024 Dual-branch subgraph-pattern attention graph neural network for link prediction

Zhongxuan Ouyang, Jinhua Wang

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161J (2024) https://doi.org/10.1117/12.3049976
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Graph data is a natural form of unstructured data, with nodes and edges representing entities and the relationships between them respectively. This type of data can be directly applied to various real-world scenarios. Graph Neural Networks (GNNs) have a natural advantage in handling graph-structured data, effectively processing the hierarchical structure of graphs to extract rich information. However, current GNNs have limitations in dealing with local information. In this paper, we discover a new attention-based network module that outperforms general GNNs. To better learn global-local information and overall hierarchical information, we propose a Dual-Branch Subgraph Pattern Attention Graph Neural Network (DB-SP-GAT). Experimental results demonstrate that DB-SP-GAT achieves superior link prediction performance across five benchmark datasets.

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

Citation Download Citation

Zhongxuan Ouyang and Jinhua Wang "Dual-branch subgraph-pattern attention graph neural network for link prediction", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161J (8 November 2024); https://doi.org/10.1117/12.3049976

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