Learning Clusters through Information Diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

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

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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

Fingerprint

Information Diffusion
Vertex of a graph
Infectious Diseases
Epidemic Model
Infection
Heuristics
Graph in graph theory
Learning

Keywords

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

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
Prokhorenkova, Liudmila ; Tikhonov, Alexey ; Litvak, Nelly. / Learning Clusters through Information Diffusion. The Web Conference 2019: Proceedings of the World Wide Web Conference, WWW 2019. New York : Association for Computing Machinery (ACM), 2019. pp. 3151-3157
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title = "Learning Clusters through Information Diffusion",
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.",
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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. Association for Computing Machinery (ACM), New York, pp. 3151-3157, World Wide Web Conference, WWW 2019, San Francisco, United States, 13/05/19. https://doi.org/10.1145/3308558.3313560

Learning Clusters through Information Diffusion. / Prokhorenkova, Liudmila; Tikhonov, Alexey; Litvak, Nelly.

The Web Conference 2019: Proceedings of the World Wide Web Conference, WWW 2019. New York : Association for Computing Machinery (ACM), 2019. p. 3151-3157.

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

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