Combining feature fusion link prediction with graph neural networks

Yamin Zheng; Yongli Wang; Xiaoze Gong

doi:10.1117/12.2673308

13 April 2023 Combining feature fusion link prediction with graph neural networks

Yamin Zheng, Yongli Wang, Xiaoze Gong

Proceedings Volume 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022); 1260513 (2023) https://doi.org/10.1117/12.2673308
Event: Second Conference on High Performance Computing and Communication Engineering, 2022, Harbin, China

Abstract

At present, Graph Neural Network (GNN) methods usually follow the node centered message passing process and rely heavily on smooth node characteristics rather than graph structure. In view of this limitation, based on the heuristic method and graph attention mechanism, a feature fusion link prediction model (SAFL) combined with graph neural network is proposed. This model extracts the enclosing subgraphs around the target, combines the attention mechanism to assign neighbor weights to learn useful structural features, considers the impact of different nodes on the link, and fuses the graph neural network with the characteristics of input nodes to predict the link. The experiment on OGB dataset shows that the proposed link prediction model based on heuristic method enhances the graph structure characteristics, effectively represents the connectivity of enclosing subgraphs, and achieves better performance in link prediction.

Citation Download Citation

Yamin Zheng, Yongli Wang, and Xiaoze Gong "Combining feature fusion link prediction with graph neural networks", Proc. SPIE 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022), 1260513 (13 April 2023); https://doi.org/10.1117/12.2673308

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