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.
This work was jointly supported by: (1) National Science Fund for Distinguished Young Scholars (No. 60525110); (2) National 973 Program (No. 2007CB307100, 2007CB307103); (3) Development Fund Project for Electronic and Information Industry (Mobile Service and Application System Based on 3G).
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Wan, L., Liao, J., Wang, C., Zhu, X. (2009). JCCM: Joint Cluster Communities on Attribute and Relationship Data in Social Networks. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_70
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DOI: https://doi.org/10.1007/978-3-642-03348-3_70
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