ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (2): 306-318.doi: 10.7544/issn1000-1239.2019.20170746

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Probability Matrix Factorization for Link Prediction Based on Information Fusion

Wang Zhiqiang1, Liang Jiye1,2, Li Ru1,2   

  1. 1(School of Computer & Information Technology, Shanxi University, Taiyuan 030006); 2(Key Laboratory of Computation Intelligence & Chinese Information Processing (Shanxi University), Ministry of Education, Taiyuan 030006)
  • Online:2019-02-01

Abstract: As one kind of typical network big data, social-information networks such as Weibo and Twitter include both the complex network structure among users and rich microblog/Tweet information published by users. It is notable that most of the existing methods only make use of the network topological information or the non-topological information for link prediction, but there is still a lack of effective methods by fusing the topological information or non-topological information in social-information networks. A link prediction method is proposed from the perspective of users’ topic by fusing users’ topic similarity in social-information networks. The method goes in accordance with the following sequence: firstly, a topic similarity between users based on users’ topic representation is defined, followed by which a topic similarity-based sparse network is constructed; secondly, the information of the following/followed network and the topic similarity-based network are fused into a unified framework of probabilistic matrix factorization, based on which the latent-feature representation of the network nodes and the linking relation parameters are obtained; finally, the linking probability between network nodes is calculated based on the obtained latent-feature representation and linking relation parameters. The proposed approach provides a general modeling strategy fusing multi-network information and a learning-based solution. Link prediction experiments are conducted on four real network datasets, i.e. Twitter and Weibo. The experimental results demonstrate that the proposed method is more effective than others.

Key words: social-information network, link prediction, probability matrix factorization, fusion model, network data analysis

CLC Number: