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Topic Preference-based Random Walk Approach for Link Prediction in Social Networks

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

Link prediction is a challenging problem in complex graph, but has many impacts on various fields, such as detecting missed linkages in general graphs, collaborative filtering in co-authorship networks, and predicting protein-protein interactions in Bioinformatics. In this paper, we present a new link prediction method applied for friend suggestion in social networks. The benefits not only help social players easily find new friends but also enhance their loyalties to the social sites. Unlike existing methods that commonly employ statistical attributes of vertices (e.g., in- and out-degree) and topological structures (e.g., distance and path), we contribute to exploit topical information extracted from users’ posted messages. We also introduce a new similarity measure that takes into account users’ topic preferences and popularity in topical domains for effective ranking associated friends. Experimental results conducted on a real Twitter data show that the proposed approach outperforms other three state-of-the-art methods in all the cases.

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Notes

  1. 1.

    https://dev.twitter.com/rest/public.

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Author information

Authors and Affiliations

  1. Massive Information and Knowledge Engineering Laboratory, Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, 10900, Thailand

    Thiamthep Khamket, Arnon Rungsawang & Bundit Manaskasemsak

Authors
  1. Thiamthep Khamket
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  2. Arnon Rungsawang
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  3. Bundit Manaskasemsak
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Corresponding author

Correspondence to Bundit Manaskasemsak .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Khamket, T., Rungsawang, A., Manaskasemsak, B. (2017). Topic Preference-based Random Walk Approach for Link Prediction in Social Networks. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

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