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Group formation in large social networks: membership, growth, and evolution (2006)
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| Venue: | IN KDD ’06: PROCEEDINGS OF THE 12TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING |
| Citations: | 496 - 19 self |
BibTeX
@INPROCEEDINGS{Backstrom06groupformation,
author = {Lars Backstrom and Dan Huttenlocher and Jon Kleinberg and Xiangyang Lan},
title = {Group formation in large social networks: membership, growth, and evolution},
booktitle = {IN KDD ’06: PROCEEDINGS OF THE 12TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING},
year = {2006},
pages = {44--54},
publisher = {ACM Press}
}
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Abstract
The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences — political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale timeresolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time? Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are
Keyphrases
group formation large social network central research issue social networking site explicit user-defined community religious denomination social science political movement on-line group new member community membership professional organization basic question social group underlying network structure friendship link digital domain conference publication structural feature large source social network subtle way fundamental example
