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Detecting communities in large networks

Author

Listed:
  • Capocci, A.
  • Servedio, V.D.P.
  • Caldarelli, G.
  • Colaiori, F.

Abstract

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Suggested Citation

  • Capocci, A. & Servedio, V.D.P. & Caldarelli, G. & Colaiori, F., 2005. "Detecting communities in large networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 669-676.
    • Handle: RePEc:eee:phsmap:v:352:y:2005:i:2:p:669-676
    DOI: 10.1016/j.physa.2004.12.050
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    Citations

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    Cited by:

    1. Nicolò Pecora & Alessandro Spelta, 2014. "Shareholding Network in the Euro Area Banking Market," DISCE - Working Papers del Dipartimento di Economia e Finanza def014, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    2. Li, Jianyu & Zhou, Jie & Luo, Xiaoyue & Yang, Zhanxin, 2012. "Chinese lexical networks: The structure, function and formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5254-5263.
    3. Jiao, Qing-Ju & Huang, Yan & Shen, Hong-Bin, 2015. "Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 363-371.
    4. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    5. Lou, Hao & Li, Shenghong & Zhao, Yuxin, 2013. "Detecting community structure using label propagation with weighted coherent neighborhood propinquity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3095-3105.
    6. Byungyun Yang & Minjun Kim & Changkyu Lee & Suyeon Hwang & Jinmu Choi, 2022. "Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    7. Pecora, Nicolò & Spelta, Alessandro, 2015. "Shareholding relationships in the Euro Area banking market: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 1-12.
    8. Shen, Yi & Pei, Wenjiang & Wang, Kai & Li, Tao & Wang, Shaoping, 2008. "Recursive filtration method for detecting community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6663-6670.
    9. Fuqiang Zhao & Lichao Zhang & Guijun Yang & Li He & Fengyu Yan, 2017. "Application Of Cut Algorithm Based On Algebraic Connectivity To Community Detection," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-18, February.
    10. Yu, Jia-Wei & Xie, Wen-Jie & Jiang, Zhi-Qiang, 2018. "Early warning model based on correlated networks in global crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1335-1343.
    11. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    12. Guangzhou Diao & Liping Zhao & Yiyong Yao, 2016. "A weighted-coupled network-based quality control method for improving key features in product manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 535-548, June.
    13. 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.
    14. repec:ctc:serie1:def14 is not listed on IDEAS
    15. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
    16. Yang, Bo & Li, Xu & Liu, Xiangwei & He, He & Chen, Wei, 2019. "Alternating between consensus and leader selection reveals community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 693-706.
    17. Luthi, Leslie & Pestelacci, Enea & Tomassini, Marco, 2008. "Cooperation and community structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 955-966.
    18. Chen, Kaiqi & Bi, Weihong, 2019. "A new genetic algorithm for community detection using matrix representation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    19. Igor Mezić & Vladimir A. Fonoberov & Maria Fonoberova & Tuhin Sahai, 2019. "Spectral Complexity of Directed Graphs and Application to Structural Decomposition," Complexity, Hindawi, vol. 2019, pages 1-18, January.
    20. Tugrul Temel & Paul Phumpiu, 2021. "Pathways to recovery from COVID-19: characterizing input–output linkages of a targeted sector," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-24, December.

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