Detecting large cohesive subgroups with high clustering coefficients in social networks |
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Institution: | 1. Department of Business and Technology Management, Korea Advanced Institute of Science and Technology, N22, 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Republic of Korea;2. School of Media and Communication, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 136-701, Republic of Korea;3. Creative Future Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Daejeon 305-700, South Korea |
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Abstract: | Clique relaxations are used in classical models of cohesive subgroups in social network analysis. Clustering coefficient was introduced more recently as a structural feature characterizing small-world networks. Noting that cohesive subgroups tend to have high clustering coefficients, this paper introduces a new clique relaxation, α-cluster, defined by enforcing a lower bound α on the clustering coefficient in the corresponding induced subgraph. Two variations of the clustering coefficient are considered, namely, the local and global clustering coefficient. Certain structural properties of α-clusters are analyzed and mathematical optimization models for determining α-clusters of the largest size in a network are developed and validated using several real-life social networks. In addition, a network clustering algorithm based on local α-clusters is proposed and successfully tested. |
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Keywords: | Cohesive subgroups Clustering coefficient Clique relaxations Optimization |
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