Learning clusters through information diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

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

8 Citations (Scopus)


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 - Proceedings of the World Wide Web Conference, WWW 2019
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages7
ISBN (Electronic)978-1-4503-6674-8
Publication statusPublished - 13 May 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: 13 May 201917 May 2019


Conference2019 World Wide Web Conference, WWW 2019
Country/TerritoryUnited States
CitySan Francisco


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


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