Learning node embeddings for influence set completion

Sergey Ivanov, Nikita Durasov, Evgeny Burnaev

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

    4 Citations (Scopus)

    Abstract

    Influence Maximization problem has found numerous applications in the real world and attracted a lot of research in the recent years. However, all previous attempts to provide a solution were based solely on the graph topology. Instead, we show how to employ recent advancement in representation learning and use node embeddings for finding solution as a supervised task. In our experiments, we show that the ranked list of nodes obtained by classifier yields better influence completion set than other baselines.

    Original languageEnglish
    Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
    EditorsHanghang Tong, Zhenhui Li, Feida Zhu, Jeffrey Yu
    PublisherIEEE Computer Society
    Pages1034-1037
    Number of pages4
    ISBN (Electronic)9781538692882
    DOIs
    Publication statusPublished - 7 Feb 2019
    Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
    Duration: 17 Nov 201820 Nov 2018

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    Volume2018-November
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
    Country/TerritorySingapore
    CitySingapore
    Period17/11/1820/11/18

    Keywords

    • influence maximziation
    • node embeddings

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