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dc.contributor.author You, Zhi-Qiang
dc.contributor.author Han, Xiao-Pu
dc.contributor.author Lü, Linyuan
dc.contributor.author Yeung, Chi Ho
dc.coverage.spatial China
dc.date.accessioned 2015-09-02T18:27:33Z
dc.date.available 2015-09-02T18:27:33Z
dc.date.issued 2015-07-15
dc.identifier doi:10.5061/dryad.pc537
dc.identifier.citation You Z, Han X, Lü L, Yeung CH (2015) Empirical studies on the network of social groups: the case of Tencent QQ. PLOS ONE 10(7): e0130538.
dc.identifier.uri http://hdl.handle.net/10255/dryad.86016
dc.description Background: Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks. Methodology/Principal Findings: In this paper, we analyze a comprehensive dataset released from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members—the hypergraph of groups, the network of groups and the user network—to reveal social interactions at microscopic and mesoscopic level. Conclusions/Significance: Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in social networks based on personal contacts.
dc.relation.haspart doi:10.5061/dryad.pc537/1
dc.relation.isreferencedby doi:10.1371/journal.pone.0130538
dc.relation.isreferencedby PMID:26176850
dc.subject social groups
dc.subject collective behaviors
dc.subject Tencent QQ
dc.subject derivative networks
dc.subject social interactions
dc.subject complex network
dc.subject social networks
dc.title Data from: Empirical studies on the network of social groups: the case of Tencent QQ
dc.type Article
prism.publicationName PLOS ONE

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Title Dataset of QQ group social network
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Description Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks. In this paper, we analyze a comprehensive dataset obtained from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members { the hypergraph of groups, the network of groups and the user network { to reveal social interactions at microscopic and mesoscopic level. Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in social networks based on personal contacts.
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