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GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction

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Abstract

Dynamic network link prediction is becoming a hot topic in network science, due to its wide applications in biology, sociology, economy and industry. However, it is a challenge since network structure evolves with time, making long-term prediction of adding/deleting links especially difficult. Inspired by the great success of deep learning frameworks, especially the convolution neural network (CNN) and long short-term memory (LSTM) network, we propose a novel end-to-end model with a Graph Convolution Network(GCN) embedded LSTM, named GC-LSTM, for dynamic network link prediction. Thereinto, LSTM is adopted as the main framework to learn the temporal features of all snapshots of a dynamic network. While for each snapshot, GCN is applied to capture the local structural properties of nodes as well as the relationship between them. One benefit is that our GC-LSTM can predict both added and removed links, making it more practical in reality, while most existing dynamic link prediction methods can only handle removed links. Extensive experiments demonstrated that GC-LSTM achieves outstanding performance and outperforms existing state-of-the-art methods.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant No. 6207-2406, the Natural Science Foundation of Zhejiang Province under Grant No. LY19F020025, the Major Special Funding for “Science and Technology Innovation 2025” in Ningbo under Grant No. 2018B10063, the National Key Research and Development Program of China under Grant No. 2018AAA0100801, the Special Technology Project for Pre-research of Military Common Information System Equipment under Grant No. JZX6Y201907010467.

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  1. Institute of Cyberspace Security and the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Jinyin Chen

  2. The College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Xueke Wang & Xuanheng Xu

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  1. Jinyin Chen
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  2. Xueke Wang
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Correspondence to Jinyin Chen.

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Chen, J., Wang, X. & Xu, X. GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Appl Intell 52, 7513–7528 (2022). https://doi.org/10.1007/s10489-021-02518-9

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