Link propagation: A fast semi-supervised learning algorithm for link prediction

Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    55 Citations (Scopus)

    Abstract

    We propose Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities. Since the proposed method can fill in missing parts of tensors, it is applicable to multi-relational domains, allowing us to handle multiple types of links simultaneously. We also give a novel efficient algorithm for Link Propagation based on an accelerated conjugate gradient method.

    Original languageEnglish
    Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
    Pages1093-1104
    Number of pages12
    Publication statusPublished - 2009
    Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
    Duration: Apr 30 2009May 2 2009

    Publication series

    NameSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
    Volume3

    Other

    Other9th SIAM International Conference on Data Mining 2009, SDM 2009
    CountryUnited States
    CitySparks, NV
    Period4/30/095/2/09

    All Science Journal Classification (ASJC) codes

    • Computational Theory and Mathematics
    • Software
    • Applied Mathematics

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