Community Detection in a Large Real-World Social Network

  • Karsten Steinhaeuser
  • Nitesh V. Chawla


Identifying meaningful community structure in social networks is a hard problem, and extreme network size or sparseness of the network compound the difficulty of the task.With a proliferation of real-world network datasets there has been an increasing demand for algorithms that work effectively and efficiently. Existing methods are limited by their computational requirements and rely heavily on the network topology, which fails in scale-free networks. Yet, in addition to the network connectivity, many datasets also include attributes of individual nodes, but current methods are unable to incorporate this data. Cognizant of these requirements we propose a simple approach that stirs away from complex algorithms, focusing instead on the edge weights; more specifically, we leverage the node attributes to compute better weights. Our experimental results on a real-world social network show that a simple thresholding method with edge weights based on node attributes is sufficient to identify a very strong community structure.


Edge Weight Community Detection Node Attribute Adjust Rand Index Network Compound 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    S. Asur, D. Ucar, S. Parthasarathy: An Ensemble Framework for Clustering Protein-Protein Interaction Graphs. In Proceedings of ISMB (2007)Google Scholar
  2. 2.
    A.-L. Barabási and E. Bonabeau: Scale-free networks. Scientific American 288 (2003) 50-59CrossRefGoogle Scholar
  3. 3.
    G. Madey, A.-L. Barabáasi, N. V. Chawla, et al: Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management. In LNCS 4487 (2007) 1090-1097Google Scholar
  4. 4.
    G. Milligan, M. Cooper: A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. Multiv. Behav. Res. bf 21 (1986) 441-458CrossRefGoogle Scholar
  5. 5.
    M. E. Newman: Detecting community structure in networks. Eur. Phys. J. bf B38 (2004) 321-330CrossRefGoogle Scholar
  6. 6.
    M. E. Newman: Finding and evaluating community structure in networks. Phys. Rev. E bf 69 (2004) 023113Google Scholar
  7. 7.
    P. Pons, M. Latapy: Computing communities in large networks using random walks. J. of Graph Alg. and App. bf 10 (2006) 191-218MathSciNetMATHGoogle Scholar
  8. 8.
    D. R. Wilson, T. R. Martinez: Improved heterogeneous distance functions. J. Art. Int. Res. bf 6 (1997) 1-34MATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Karsten Steinhaeuser
    • 1
  • Nitesh V. Chawla
    • 1
  1. 1.University of Notre DameUSA

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