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A Triad Percolation Method for Detecting Communities in Social Networks
Author(s) -
Zhiwei Zhang,
Lin Cui,
Zhenggao Pan,
Aidong Fang,
Haiyang Zhang
Publication year - 2018
Publication title -
data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.358
H-Index - 21
ISSN - 1683-1470
DOI - 10.5334/dsj-2018-030
Subject(s) - triad (sociology) , computer science , percolation (cognitive psychology) , benchmark (surveying) , social network (sociolinguistics) , community structure , data mining , data science , psychology , world wide web , mathematics , statistics , geography , cartography , social media , neuroscience , psychoanalysis
For the purpose of detecting communities in social networks, a triad percolation method is proposed, which first locates all close-triads and open-triads from a social network, then a specified close-triad or open-triad is selected as the seed to expand by utilizing the triad percolation method, such that a community is found when this expanding process meet a particular threshold. This approach can efficiently detect communities not only from a densely social network, but also from the sparsely one. Experimental results performing on real-world social benchmark networks and artificially simulated networks give a satisfactory correspondence.

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