Feature fusion graph attention network for link prediction

Xuan Zhang; WangQun Chen; FuQiang Lin; XinYi Chen; Bo Liu

doi:10.1117/12.2626465

21 December 2021 Feature fusion graph attention network for link prediction

Xuan Zhang, WangQun Chen, FuQiang Lin, XinYi Chen, Bo Liu

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121561C (2021) https://doi.org/10.1117/12.2626465
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Link prediction aims to predict whether two nodes in a network are likely to be connected, which is widely used to deal with complex networks, such as biological network analysis and social network recommendation. Most work uses network structure data and node attribute information to predict links. However, they lose too much of the network structure characteristics in network processing and do not distinguish the different importance of neighbor nodes. To address these problems, we propose a feature fusion graph attention network for link prediction (FFGAT), which trains in batches by extracting associated subgraphs. In the extracted associated subgraph, we use the double-radius node labeling method to mark the structure label for all nodes, which is used to enhance the network structure representation ability of the model. In feature fusion, we introduce a multi-head graph attention network to aggregate the node attributes and network structure attributes of multi-order neighbors. The classification predictor uses the generated node embedding to predict the link between node pairs. Experiments are performed on seven commonly used link prediction datasets. Compared with the existing baselines, our FFGAT achieves the state-of-the-art performance.

Citation Download Citation

Xuan Zhang, WangQun Chen, FuQiang Lin, XinYi Chen, and Bo Liu "Feature fusion graph attention network for link prediction", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121561C (21 December 2021); https://doi.org/10.1117/12.2626465

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