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MvInf: Social Influence Prediction with Multi-view Graph Attention Learning

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Abstract

The potential impact of social influence prediction has become a hot topic in the current graph data mining area. This paper proposes a deep learning framework named Multi-view Influence prediction network (MvInf) which combines multi-view learning and graph attention neural network together to address the problem of social influence prediction. MvInf takes different attribute features of users as the input of graph attention network and uses the complementarity and consistency between different views to enhance learning performance and thus to better predict user behavior. Experiments performed on four standard datasets (Open Academic Graph, Twitter, Weibo, and Digg) demonstrate that the proposed MvInf model can obtain better performance than previous single view-based approach.

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, No. 111 Jiulong Road, Anhui, Hefei, China

    Huifang Xu & Bo Jiang

  2. School of Data Science, Chinese University of Hong Kong, Shenzhen, China

    Chris Ding

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  1. Huifang Xu
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  2. Bo Jiang
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  3. Chris Ding
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Correspondence to Huifang Xu.

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Huifang Xu declares that she has no conflict of interest. Bo Jiang declares that he has no conflict of interest. Chris H. Q. Ding declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Xu, H., Jiang, B. & Ding, C. MvInf: Social Influence Prediction with Multi-view Graph Attention Learning. Cogn Comput 14, 1182–1188 (2022). https://doi.org/10.1007/s12559-021-09822-z

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