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A fast and efficient heuristic algorithm for detecting community structures in complex networks

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  • Chen, Duanbing
  • Fu, Yan
  • Shang, Mingsheng

Abstract

Community structure is an important property of complex networks. How to detect the communities is significant for understanding the network structure and to analyze the network properties. Many algorithms, such as K–L and GN, have been proposed to detect community structures in complex networks. According to daily experience, a community should have many nodes and connections. Based on these principles and existing researches, a fast and efficient algorithm for detecting community structures in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to this community. Four real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient for detecting community structures in complex networks.

Suggested Citation

  • Chen, Duanbing & Fu, Yan & Shang, Mingsheng, 2009. "A fast and efficient heuristic algorithm for detecting community structures in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2741-2749.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:13:p:2741-2749
    DOI: 10.1016/j.physa.2009.03.022
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    References listed on IDEAS

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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906, Decembrie.
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    Citations

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    Cited by:

    1. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    2. Chen, Duanbing & Shang, Mingsheng & Lv, Zehua & Fu, Yan, 2010. "Detecting overlapping communities of weighted networks via a local algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4177-4187.
    3. Cui, Yaozu & Wang, Xingyuan & Li, Junqiu, 2014. "Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 85-91.
    4. Liu, Xu & Forrest, Jeffrey Yi-Lin & Luo, Qiang & Yi, Dong-Yun, 2012. "Detecting community structure using biased random merging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1797-1810.
    5. Shang, Jiaxing & Liu, Lianchen & Li, Xin & Xie, Feng & Wu, Cheng, 2016. "Targeted revision: A learning-based approach for incremental community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 70-85.
    6. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.
    7. Angelito Calma & José Martí-Parreño & Martin Davies, 2019. "Journal of the Academy of Marketing Science 1973–2018: an analytical retrospective," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 879-908, May.
    8. 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.
    9. Li, Junqiu & Wang, Xingyuan & Eustace, Justine, 2013. "Detecting overlapping communities by seed community in weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6125-6134.
    10. Li, Junqiu & Wang, Xingyuan & Cui, Yaozu, 2014. "Uncovering the overlapping community structure of complex networks by maximal cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 398-406.
    11. Gong, Maoguo & Ma, Lijia & Zhang, Qingfu & Jiao, Licheng, 2012. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4050-4060.
    12. Wang, Xingyuan & Li, Junqiu, 2013. "Detecting communities by the core-vertex and intimate degree in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2555-2563.

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