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Detecting communities in clustered networks based on group action on set

Author

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  • Zhang, Zhanli
  • Jiang, Xin
  • Ma, Lili
  • Tang, Shaoting
  • Zheng, Zhiming

Abstract

In this paper, we propose a well targeted algorithm (GAS algorithm) for detecting communities in high clustered networks by presenting group action technology on community division. During the processing of this algorithm, the underlying community structure of a clustered network emerges simultaneously as the corresponding partition of orbits by the permutation groups acting on the node set are achieved. As the derivation of the orbit partition, an algebraic structure r-cycle can be considered as the origin of the community. To be a priori estimation for the community structure of the algorithm, the community separability is introduced to indicate whether a network has distinct community structure. By executing the algorithm on several typical networks and the LFR benchmark, it shows that this GAS algorithm can detect communities accurately and effectively in high clustered networks. Furthermore, we compare the GAS algorithm and the clique percolation algorithm on the LFR benchmark. It is shown that the GAS algorithm is more accurate at detecting non-overlapping communities in clustered networks. It is suggested that algebraic techniques can uncover fresh light on detecting communities in complex networks.

Suggested Citation

  • Zhang, Zhanli & Jiang, Xin & Ma, Lili & Tang, Shaoting & Zheng, Zhiming, 2011. "Detecting communities in clustered networks based on group action on set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1171-1181.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:6:p:1171-1181
    DOI: 10.1016/j.physa.2010.11.029
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    Cited by:

    1. Ma, Lili & Jiang, Xin & Wu, Kaiyuan & Zhang, Zhanli & Tang, Shaoting & Zheng, Zhiming, 2012. "Surveying network community structure in the hidden metric space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 371-378.

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