Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

• Published in: IEEE access 2021, Vol.9, p.412-420
• Main Authors: Xia, Tianyu, Gu, Yijun, Yin, Dechun
• Format: Article
• Language: English
• Subjects: Biological system modeling; Complex networks; dynamic social network; Embedding; graph representation learning; Graph representations; Graphical representations; Heuristic algorithms; Indexes; Learning; Link prediction; Multiplexing; Prediction models; Predictive models; Social networking (online); Social networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3046526

In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What's more, the t+1 time step embedding vector was used to validate t time step prediction effect and the proposed model performed better than traditional graph representation learning methods.