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Discrete Improved Grey Wolf Optimizer for Community Detection

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Abstract

Detecting communities in real and complex networks is a highly contested topic in network analysis. Although many metaheuristic-based algorithms for community detection have been proposed, they still cannot effectively fulfill large-scale and real-world networks. Thus, this paper presents a new discrete version of the Improved Grey Wolf Optimizer (I-GWO) algorithm named DI-GWOCD for effectively detecting communities of different networks. In the proposed DI-GWOCD algorithm, I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution. Then a novel Binary Distance Vector (BDV) is introduced to calculate the wolves’ distances and adapt I-GWO for solving the discrete community detection problem. The performance of the proposed DI-GWOCD was evaluated in terms of modularity, NMI, and the number of detected communities conducted by some well-known real-world network datasets. The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests. The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

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All data generated or analyzed during this study are included in this published article.

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Acknowledgements

The authors would like to thank all anonymous reviewers for their valuable suggestions on this study.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131, Iran

    Mohammad H. Nadimi-Shahraki, Ebrahim Moeini & Shokooh Taghian

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131, Iran

    Mohammad H. Nadimi-Shahraki, Ebrahim Moeini & Shokooh Taghian

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, 4006, Australia

    Seyedali Mirjalili

  4. Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

    Seyedali Mirjalili

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  1. Mohammad H. Nadimi-Shahraki
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Nadimi-Shahraki, M.H., Moeini, E., Taghian, S. et al. Discrete Improved Grey Wolf Optimizer for Community Detection. J Bionic Eng 20, 2331–2358 (2023). https://doi.org/10.1007/s42235-023-00387-1

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