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An information-theoretic approach for detecting communities in networks

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  • Yongli Li
  • Chong Wu
  • Zizheng Wang

Abstract

Detecting communities in a network can be helpful to comprehend its structure and understand its function. The detecting-communities approach, in essence, is a model of classification and belongs to the scope of social methodology. In this paper, we view the community description of a network as a lossy compression of that network’s information, and develop an information-theoretic foundation accordingly for the concept of community in networks. We present an optimization model and its algorithm to identify the communities by finding an optimal compression of the network. We also illustrate the availability of this approach by an artificial example, compare its accuracy and algorithm complexity with the other classical approaches of this field by a series of simulated networks with different parameters, and demonstrate its application in a real-world network. The tests show that the proposed method is a good one for detecting the communities and finding the proper community number in the unweighted and weighted networks. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Yongli Li & Chong Wu & Zizheng Wang, 2015. "An information-theoretic approach for detecting communities in networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1719-1733, July.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:4:p:1719-1733
    DOI: 10.1007/s11135-014-9996-8
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Rosario D’Agata & Simona Gozzo & Venera Tomaselli, 2013. "Network analysis approach to map tourism mobility," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3167-3184, October.
    3. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    4. Francesca Pallotti & Alessandro Lomi & Daniele Mascia, 2013. "From network ties to network structures: Exponential Random Graph Models of interorganizational relations," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1665-1685, April.
    5. Han Park, 2012. "How do social scientists use link data from search engines to understand Internet-based political and electoral communication?," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(2), pages 679-693, February.
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