Learning clusters through information diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Samenvatting

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.

Originele taal-2Engels
TitelThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
Plaats van productieNew York
UitgeverijAssociation for Computing Machinery, Inc
Pagina's3151-3157
Aantal pagina's7
ISBN van elektronische versie978-1-4503-6674-8
DOI's
StatusGepubliceerd - 13 mei 2019
Evenement2019 World Wide Web Conference, WWW 2019 - San Francisco, Verenigde Staten van Amerika
Duur: 13 mei 201917 mei 2019

Congres

Congres2019 World Wide Web Conference, WWW 2019
LandVerenigde Staten van Amerika
StadSan Francisco
Periode13/05/1917/05/19

Citeer dit

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 (blz. 3151-3157). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313560