Learning Clusters through Information Diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

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

    9 Citations (Scopus)
    270 Downloads (Pure)


    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
    Number of pages7
    ISBN (Electronic)9781450366748
    ISBN (Print)978-1-4503-6674-8
    Publication statusPublished - 13 May 2019
    EventWorld Wide Web Conference, WWW 2019 - Hyatt Regency San Francisco hotel, San Francisco, United States
    Duration: 13 May 201917 May 2019


    ConferenceWorld Wide Web Conference, WWW 2019
    Abbreviated titleWWW 2019
    Country/TerritoryUnited States
    CitySan Francisco
    Internet address


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


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