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A new nature-inspired optimization for community discovery in complex networks

  • Regular Article - Statistical and Nonlinear Physics
  • Published:
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Abstract

The community structure, owing to its significant status, is of extraordinary significance in comprehending and detecting inherent functions in real networks. However, the community structures are always hard to be identified, and whether the existing algorithms are based on optimization or heuristics, the robustness and accuracy should be improved. The physarum (i.e., slime molds with multi heads) has proved its ability to produce foraging networks. Therefore, we adopt physarum so that the optimization-based community detection algorithms can work more efficiently. Specifically, a physarum-based network model (pnm), which is capable of identifying inter-edges of the community in a network, is used to optimize the prior knowledge of existing evolutional algorithms (i.e., genetic algorithm, particle swarm optimization algorithm and ant colony algorithm). the optimized algorithms have been compared with some advanced methods in synthetic and real networks. experimental results have verified the effectiveness of the proposed method.

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: All network used in Sec. 4.1 are public datasets and cited in terms of footnote in Page 6].

Notes

  1. http://www-personal.umich.edu/~mejn/netdata/.

  2. http://vlado.fmf.uni-lj.si/vlado/vladonet.htm.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2020AAA0107700), National Natural Science Foundation of China (Nos. 61976181, 11931015, U1803263), Fok Ying-Tong Education Foundation, China (No. 171105), Key Technology Research and Development Program of Science and Technology Scientific and Technological Innovation Team of Shaanxi Province (No. 2020TD-013), the Science and Technology Foundation of Guizhou (No. QKHJC20181083) and the Science and Technology Support Program of Guizhou (No. QKHZC2021YB531).

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Authors and Affiliations

  1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Xiaoyu Li

  2. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Chao Gao, Zhen Wang & Xianghua Li

  3. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Songxin Wang

  4. School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Chen Liu

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  1. Xiaoyu Li
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  2. Chao Gao
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  3. Songxin Wang
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  4. Zhen Wang
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  5. Chen Liu
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  6. Xianghua Li
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Correspondence to Xianghua Li.

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Li, X., Gao, C., Wang, S. et al. A new nature-inspired optimization for community discovery in complex networks. Eur. Phys. J. B 94, 137 (2021). https://doi.org/10.1140/epjb/s10051-021-00122-x

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