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Influence propagation model for clique-based community detection in social networks
journal contribution
posted on 2018-06-01, 00:00 authored by Noha Alduaiji, Amitava Datta, Jianxin LiJianxin LiSocial media community detection is a fundamental challenge in social data analytics, in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, the majority of research has been based on social networks with static structures. Our findings within large social networks, such as Twitter, show that only a few users have interactions or communications at fixed time intervals. Finding active communities that demonstrate constant interactions between its members comprises a reasonable perspective. Communities examined from this perspective will provide time-variant social relationships, which may greatly improve the applicability of social data analytics. In this paper, we address the problem of temporal interaction-biased community detection using a four-step process. First, we develop a partition approach using an objective function based on clique structure, to enhance the time efficiency of our methodology. Second, we develop an influence propagation model that gives greatest weight to active edges or to inactive edges in close proximity to active edges. Third, we develop expansion-driven algorithms to efficiently find the activity-biased densest community. Finally, we verify the effectiveness of the extended community metric and the efficiency of the algorithms using three real data sets and a case study conducted on Twitter dynamic data set.
History
Journal
IEEE transactions on computational social systemsVolume
5Issue
2Pagination
563 - 575Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
eISSN
2329-924XLanguage
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2018, IEEEUsage metrics
Categories
Keywords
community detectioninfluence propagation modelpartitioning algorithmsocial network analysistemporal interactionsScience & TechnologyTechnologyComputer Science, CyberneticsComputer Science, Information SystemsComputer ScienceInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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