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Efficient stepwise detection of communities in temporal networks

Author

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  • He, Jialin
  • Chen, Duanbing
  • Sun, Chongjing
  • Fu, Yan
  • Li, Wenjun

Abstract

In temporal networks, dynamic community detection is composed of two separate stages: (i) community detection at each time step; (ii) community matching across time steps. In the traditional methods, the community matching across time steps is based on nodes, which is time consuming. In this paper, we suggest a simple method which takes advantage of historic community information to detect dynamic communities. After dividing each community at previous time step into a few modules, we cannot only use these modules to detect communities at current time step but also map communities across time steps. Results on synthetic and real networks demonstrate that our method cannot only maintain the quality of communities but also improve the efficiency of community matching significantly.

Suggested Citation

  • He, Jialin & Chen, Duanbing & Sun, Chongjing & Fu, Yan & Li, Wenjun, 2017. "Efficient stepwise detection of communities in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 438-446.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:438-446
    DOI: 10.1016/j.physa.2016.11.019
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    References listed on IDEAS

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    1. He, Jialin & Chen, Duanbing, 2015. "A fast algorithm for community detection in temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 87-94.
    2. 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.
    3. Guillaume Laurent & Jari Saramäki & Márton Karsai, 2015. "From calls to communities: a model for time-varying social networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(11), pages 1-10, November.
    4. Katalin Orosz & Illés J. Farkas & Péter Pollner, 2016. "Quantifying the changing role of past publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 829-853, August.
    5. Thang N. Dinh & Incheol Shin & Nhi K. Thai & My T. Thai & Taieb Znati, 2010. "A General Approach for Modules Identification in Evolving Networks," Springer Optimization and Its Applications, in: Michael J. Hirsch & Panos M. Pardalos & Robert Murphey (ed.), Dynamics of Information Systems, chapter 0, pages 83-100, Springer.
    6. Ulrik Brandes & Jürgen Lerner, 2010. "Structural Similarity: Spectral Methods for Relaxed Blockmodeling," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 279-306, November.
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    Cited by:

    1. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.

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