DSINE: Deep Structural Influence Learning via Network Embedding

Structural representations of user social influence are critical for a variety of applications such as viral marketing and recommendation products. However, existing studies only focus on capturing and preserving the structure of relations, and ignore the diversity of influence relations patterns among users. To this end, we propose a deep structural influence learning model to learn social influence structure via mining rich features of each user, and fuse information from the aligned selfnetwork component for preserving global and local structure of the influence relations among users. Experiments on two real-world datasets demonstrate that the proposed model outperforms the state-of-the-art algorithms for learning rich representations in multi-label classification task.

Authors

• Jianjun Wu
• Ying Sha Key Laboratory of Smart Farming for Agricultural Animals, Wuhan, China Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, China Hubei Engineering Technology Research Center of Agricultural Big Data, Wuhan, China College of Informatics, Huazhong Agricultural University, Wuhan, China
• Bo Jiang Didi Chuxing
• Jianlong Tan

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