期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 25, 期 2, 页码 345-369出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-009-0251-x
关键词
Social network analysis; Clustering; Graph partitioning
资金
- Science Foundation Ireland [05/IN.1/I24, 08/SRC/I407]
- Enterprise Ireland [PC/2007/010]
- Science Foundation Ireland (SFI) [05/IN.1/I24] Funding Source: Science Foundation Ireland (SFI)
The identification of cohesive communities is a key process in social network analysis. However, the algorithms that are effective for finding communities do not scale well to very large problems, as their time complexity is worse than linear in the number of edges in the graph. This is an important issue for those interested in applying social network analysis techniques to very large networks, such as networks of mobile phone subscribers. In this respect, the contributions of this paper are twofold. First, we demonstrate these scaling issues using a prominent community-finding algorithm as a case study. Then, we show that a two-stage process, whereby the network is first decomposed into manageable subnetworks using a multilevel graph partitioning procedure, is effective in finding communities in networks with more than 10(6) nodes.
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