JCCM: Joint Cluster Communities on Attribute and Relationship Data in Social Networks

Authors:
Li Wan;Jianxin Liao;Chun Wang;Xiaomin Zhu
Affiliations:
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876 and EBUPT Information Technology Co., Ltd, Beijing 100083;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876 and EBUPT Information Technology Co., Ltd, Beijing 100083;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876 and EBUPT Information Technology Co., Ltd, Beijing 100083;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876 and EBUPT Information Technology Co., Ltd, Beijing 100083
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

JCCM (Joint Clustering Coefficient Method) algorithm was proposed to identify communities which are cohesive on both attribute and relationship data in social networks. JCCM is a two-step algorithm: In the first step, it clusters tightly cohesive cliques as community cores and we proposed a heuristic method to identify community cores with a probabilistic guarantee to find out all community cores. In the second step, JCCM assigns the community cores and peripheral actors into different communities in a top-down manner resulting in a dendrogram and the final clustering is determined by our objective function, namely Joint Clustering Coefficient (JCC). To consider the power of actors in different roles in community identification, we defined two regimes of communities, namely "union" and "autarchy". Experiments demonstrated that JCCM performs better than existing algorithms and confirmed that attribute and relationship data indeed contain complementary information which helps to identify communities.