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

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Advanced Data Mining and Applications (ADMA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

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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.

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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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 100876, Beijing

    Li Wan, Jianxin Liao, Chun Wang & Xiaomin Zhu

  2. EBUPT Information Technology Co., Ltd, 100083, Beijing

    Li Wan, Jianxin Liao, Chun Wang & Xiaomin Zhu

Authors
  1. Li Wan
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  2. Jianxin Liao
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  3. Chun Wang
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  4. Xiaomin Zhu
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Editor information

Editors and Affiliations

  1. Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Xinjiekouwai Ave. 19, 100875, Beijing, China

    Ronghuai Huang

  2. The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong, Hong Kong

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  4. Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João Gama

  5. School of Information, Zhongguancum, Renmin University, 100872, Beijing, China

    Xiaofeng Meng

  6. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, St. Lucia, Queensland, Australia

    Xue Li

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© 2009 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

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