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Handbook of Optimization in Complex Networks pp 307–347Cite as

On Detection of Community Structure in Dynamic Social Networks

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 58))

Abstract

Community structure is a very special and interesting property of social networks. Knowledge of network community structure not only provides us key insights into developing more social-aware strategies for social network problems, but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments in cellular networks. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social activities and interactions tend to come and go rapidly. Can we quickly and efficiently identify the network community structure? Can we adaptively update this structure based on its history instead of recomputing from scratch?In this chapter, we present two methods for detecting community structures on social networks. First, we introduce Quick Community Adaptation (QCA), an adaptive modularity-based method for identifying and tracing the discrete community structure of dynamic social networks. This approach has not only the power of quickly and efficiently updating the network structure by only using the identified structures, but also the ability of tracing the evolution of its communities over time. Next, we present DOCA, an quick method for revealing the overlapping network communities that can be implemented in a decentralized manner. To illustrate the effectiveness of our methods, we extensively test QCA and DOCA on not only synthesized but also on real-world dynamic social networks including ENRON email network, arXiv e-print citation network and Facebook network. Finally, we demonstrate the bright applicability of our methods via two realistic applications on routing strategies in MANETs and worm containment on online social networks.

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Author information

Authors and Affiliations

  1. CISE Department, University of Florida, Gainesville, Florida, USA

    Nam P. Nguyen, Ying Xuan & My T. Thai

Authors
  1. Nam P. Nguyen
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  2. Ying Xuan
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  3. My T. Thai
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Corresponding author

Correspondence to Nam P. Nguyen .

Editor information

Editors and Affiliations

  1. Science and Engineering, Department of Computer and Information, University of Florida, Gainesville, 3260, Florida, USA

    My T. Thai

  2. Dept. Industrial & Systems, Engineering, University of Florida, Weil Hall 303, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

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Nguyen, N.P., Xuan, Y., Thai, M.T. (2012). On Detection of Community Structure in Dynamic Social Networks. In: Thai, M., Pardalos, P. (eds) Handbook of Optimization in Complex Networks. Springer Optimization and Its Applications(), vol 58. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0857-4_11

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