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A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute Information for Link Prediction

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Mobile Multimedia Communications (MobiMedia 2022)

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Abstract

Link prediction is a widely studied problem and receives considerable attention in data mining and machine learning fields. How to efficiently predict missing or hidden edges in the network is a problem that link prediction needs to solve. Traditional link prediction only focuses on the information of network topology and ignores some non-topological information, which makes the prediction performance of algorithm decline rapidly when encountering extremely sparse network. To compensate for this deficiency, this paper proposes a joint weighted nonnegative matrix factorization model for link prediction via incorporates attribute information. By designing a weighted matrix to process the attribute information of each node, both the structure and attribute information fused into the nonnegative matrix factorization framework can fully play a guiding role in the link prediction task, thus solving the problem of structure sparsity and improving the prediction performance of the algorithm. Extensive experiments on five attribute networks demonstrate that the proposed model has better prediction performance than the dozen benchmark methods and the state-of-the-art link prediction algorithms.

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Acknowledgments

We would like to thank the anonymous reviewers for their contributions. This research was supported by the Teaching Reform Research Project of Qinghai Minzu University, China (2021-JYYB-009).

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Authors and Affiliations

  1. School of Computer Science, Qinghai Minzu University, Xining, 810007, China

    Minghu Tang

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  1. Minghu Tang
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Correspondence to Minghu Tang .

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Editors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Yan Chenggang

  2. University of Massachusetts - Dartmouth, Dartmouth, MA, USA

    Wang Honggang

  3. Harbin Engineering University, Harbin, China

    Lin Yun

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Tang, M. (2022). A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute Information for Link Prediction. In: Chenggang, Y., Honggang, W., Yun, L. (eds) Mobile Multimedia Communications. MobiMedia 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-031-23902-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-23902-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23901-4

  • Online ISBN: 978-3-031-23902-1

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