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Evolution of friendship: A case study of MobiClique

    Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

    6 Citations (Scopus)

    Abstract

    Understanding the evolution of relationship among users, through generic interactions, is the key driving force to this study. We model the evolution of friendship in the social network of MobiClique using observations of interactions among users. MobiClique is a mobile ad-hoc network setting where Bluetooth enabled mobile devices communicate directly with each other as they meet opportunistically. We first apply existing topological methods to predict future friendship in MobiClique and then compare the results with the proposed interaction-based method. Our approach combines four types of user activity information to measure the similarity between users at any specific time. We also define the temporal accuracy evaluation metric and show that interaction data with temporal information is a good indicator to predict temporal social ties. The experimental evaluation suggests that the well-known static topological metrics do not perform well in ad-hoc network scenario. The results suggest that to accurately predict evolution of friendship, or topology of the network, it is necessary to utilise some interaction information.

    Original languageEnglish
    Title of host publicationACM International Conference on Computing Frontiers 2017, CF 2017
    PublisherAssociation for Computing Machinery
    Pages267-270
    Number of pages4
    ISBN (Electronic)9781450344876
    DOIs
    Publication statusPublished - 15 May 2017
    Event14th ACM International Conference on Computing Frontiers, CF 2017 - Siena, Italy
    Duration: 15 May 2017 → 17 May 2017

    Publication series

    NameACM International Conference on Computing Frontiers 2017, CF 2017

    Conference

    Conference14th ACM International Conference on Computing Frontiers, CF 2017
    Country/TerritoryItaly
    CitySiena
    Period15/05/17 → 17/05/17

    Bibliographical note

    Publisher Copyright:
    © 2017 ACM.

    Keywords

    • Community detection
    • Link prediction
    • Social network anlaysis

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