Detection of temporal communities in mobile social networks

Mengni Ruan, Huan Zhou, Dawei Li, Xuxun Liu, Qingyong Deng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1021-1026
Number of pages6
ISBN (Electronic)9781728186955
DOIs
StatePublished - Jul 2020
Event2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada
Duration: 6 Jul 20209 Jul 2020

Publication series

NameIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Conference

Conference2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
CountryCanada
CityToronto
Period6/07/209/07/20

Keywords

  • Community Similarity
  • Mobile Social Networks
  • Network Model
  • Temporal Closeness
  • Temporal Community Detection

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