Learning Clusters through Information Diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

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

    2 Citations (Scopus)
    71 Downloads (Pure)

    Abstract

    When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.
    Original languageEnglish
    Title of host publicationThe Web Conference 2019
    Subtitle of host publicationProceedings of the World Wide Web Conference, WWW 2019
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Pages3151-3157
    Number of pages7
    ISBN (Print)978-1-4503-6674-8
    DOIs
    Publication statusPublished - 2019
    EventWorld Wide Web Conference, WWW 2019 - Hyatt Regency San Francisco hotel, San Francisco, United States
    Duration: 13 May 201917 May 2019
    https://www2019.thewebconf.org/

    Conference

    ConferenceWorld Wide Web Conference, WWW 2019
    Abbreviated titleWWW 2019
    CountryUnited States
    CitySan Francisco
    Period13/05/1917/05/19
    Internet address

    Keywords

    • Community detection
    • Information propagation
    • Information cascades
    • Network inference
    • Likelihood optimization

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  • Cite this

    Prokhorenkova, L., Tikhonov, A., & Litvak, N. (2019). Learning Clusters through Information Diffusion. In The Web Conference 2019: Proceedings of the World Wide Web Conference, WWW 2019 (pp. 3151-3157). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3308558.3313560