Leveraging node attributes for incomplete relational data

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

    Abstract

    Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.

    Original languageEnglish
    Title of host publication34th International Conference on Machine Learning (ICML 2017)
    Subtitle of host publicationSydney, Australia - 6-11 August 2017
    EditorsDoina Precup, Yee Whye Teh
    Place of PublicationStroudsburg PA USA
    PublisherInternational Machine Learning Society (IMLS)
    Pages6176-6185
    Number of pages10
    Volume8
    ISBN (Print)9781510855144
    Publication statusPublished - 6 Aug 2017
    EventInternational Conference on Machine Learning 2017 - International Convention Centre , Sydney , Australia
    Duration: 6 Aug 201711 Aug 2018
    Conference number: 34th
    https://icml.cc/Conferences/2017
    https://2017.icml.cc/

    Conference

    ConferenceInternational Conference on Machine Learning 2017
    Abbreviated titleICML 2017
    CountryAustralia
    CitySydney
    Period6/08/1711/08/18
    Internet address

    Cite this

    Zhao, H., Du, L., & Buntine, W. (2017). Leveraging node attributes for incomplete relational data. In D. Precup, & Y. W. Teh (Eds.), 34th International Conference on Machine Learning (ICML 2017): Sydney, Australia - 6-11 August 2017 (Vol. 8, pp. 6176-6185). Stroudsburg PA USA: International Machine Learning Society (IMLS).
    Zhao, He ; Du, Lan ; Buntine, Wray. / Leveraging node attributes for incomplete relational data. 34th International Conference on Machine Learning (ICML 2017): Sydney, Australia - 6-11 August 2017. editor / Doina Precup ; Yee Whye Teh. Vol. 8 Stroudsburg PA USA : International Machine Learning Society (IMLS), 2017. pp. 6176-6185
    @inproceedings{509afd2f5fdd4d409106337e275c0160,
    title = "Leveraging node attributes for incomplete relational data",
    abstract = "Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.",
    author = "He Zhao and Lan Du and Wray Buntine",
    year = "2017",
    month = "8",
    day = "6",
    language = "English",
    isbn = "9781510855144",
    volume = "8",
    pages = "6176--6185",
    editor = "Doina Precup and Teh, {Yee Whye}",
    booktitle = "34th International Conference on Machine Learning (ICML 2017)",
    publisher = "International Machine Learning Society (IMLS)",

    }

    Zhao, H, Du, L & Buntine, W 2017, Leveraging node attributes for incomplete relational data. in D Precup & YW Teh (eds), 34th International Conference on Machine Learning (ICML 2017): Sydney, Australia - 6-11 August 2017. vol. 8, International Machine Learning Society (IMLS), Stroudsburg PA USA, pp. 6176-6185, International Conference on Machine Learning 2017, Sydney , Australia, 6/08/17.

    Leveraging node attributes for incomplete relational data. / Zhao, He; Du, Lan; Buntine, Wray.

    34th International Conference on Machine Learning (ICML 2017): Sydney, Australia - 6-11 August 2017. ed. / Doina Precup; Yee Whye Teh. Vol. 8 Stroudsburg PA USA : International Machine Learning Society (IMLS), 2017. p. 6176-6185.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

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    N2 - Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.

    AB - Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.

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    Zhao H, Du L, Buntine W. Leveraging node attributes for incomplete relational data. In Precup D, Teh YW, editors, 34th International Conference on Machine Learning (ICML 2017): Sydney, Australia - 6-11 August 2017. Vol. 8. Stroudsburg PA USA: International Machine Learning Society (IMLS). 2017. p. 6176-6185