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A community detection method based on local similarity and degree clustering information

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  • Wang, Tao
  • Yin, Liyan
  • Wang, Xiaoxia

Abstract

Community detection is of great importance to understand the structures and functions of networks. In this paper, a novel algorithm is proposed based on local similarity and degree clustering information. Local similarity is employed to measure the similarity between nodes and their neighbors in order to form communities within which nodes are closely connected. Degree clustering information, a hybrid criterion combining local neighborhood ratio with degree ratio, make a large number of nodes with low degree to embrace a small amount of nodes with high degree. Furthermore, each node in small scale communities has the duty to try to connect the nodes with high degree to expand communities, and finally the optimal community structure can be obtained. Simulation results on real and artificial networks show that the proposed algorithm has the excellent performance in terms of accuracy.

Suggested Citation

  • Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1344-1354
    DOI: 10.1016/j.physa.2017.08.090
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    1. L. Šubelj & M. Bajec, 2012. "Ubiquitousness of link-density and link-pattern communities in real-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(1), pages 1-11, January.
    2. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    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. Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
    5. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    6. Duncan J. Watts, 2007. "A twenty-first century science," Nature, Nature, vol. 445(7127), pages 489-489, February.
    7. Jianbin Huang & Heli Sun & Yaguang Liu & Qinbao Song & Tim Weninger, 2011. "Towards Online Multiresolution Community Detection in Large-Scale Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    8. Antonio Perianes-Rodríguez & Carlos Olmeda-Gómez & Félix Moya-Anegón, 2010. "Detecting, identifying and visualizing research groups in co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 307-319, February.
    9. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    10. Gong, Maoguo & Liu, Jie & Ma, Lijia & Cai, Qing & Jiao, Licheng, 2014. "Novel heuristic density-based method for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 71-84.
    11. Jin, Hong & Wang, Shuliang & Li, Chenyang, 2013. "Community detection in complex networks by density-based clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4606-4618.
    12. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
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    7. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    8. Cai, Biao & Wang, Yanpeng & Zeng, Lina & Hu, Yanmei & Li, Hongjun, 2020. "Edge classification based on Convolutional Neural Networks for community detection in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
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