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

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

    研究成果: Chapter in Book/Report/Conference proceedingConference contribution

    57 被引用数 (Scopus)

    抄録

    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.

    本文言語英語
    ホスト出版物のタイトルSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
    ページ1093-1104
    ページ数12
    出版ステータス出版済み - 2009
    イベント9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, 米国
    継続期間: 4 30 20095 2 2009

    出版物シリーズ

    名前Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
    3

    その他

    その他9th SIAM International Conference on Data Mining 2009, SDM 2009
    Country米国
    CitySparks, NV
    Period4/30/095/2/09

    All Science Journal Classification (ASJC) codes

    • Computational Theory and Mathematics
    • Software
    • Applied Mathematics

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