Using The Gray Wolf Optimization Algorithm for Community Detection

Subject Areas : Majlesi Journal of Telecommunication Devices

Maliheh  Ghasemzadeh ¹ (Department of metallurgical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran)
Mohammad Amin   Ghasemzadeh ² (Master of Electrical Engineering, CQUniversity, Melbourne, Australia)

Received: 2024-02-16 Accepted : 2024-04-28 Published : 2024-06-11

Keywords: community detection, gray wolf algorithm, label propagation algorithm, optimization,

Abstract :

In today's world, networks play a very important role in people's lives. One of the important issues related to networks is the issue of detecting communities. These communities are also called groups and clusters. Communities include nodes that are closely related to each other. Most of the nodes that are members of a community have common properties. In social networks, it is important to detect the community in order to analyze the network and it is a very important tool to understand the information of the network and its structure. Studying community detection has garnered significant interest in last few years, leading to the development of numerous algorithms in this area. this research, we used the gray wolf meta-heuristic algorithm and improved it with operators such as mutation, combination, and local search, and also improved the final solution of the gray wolf algorithm with the label propagation algorithm to detect communities. Experiments showed that the proposed method has high accuracy and also due to the applied techniques, the problem converges to the best solution very quickly.

References:

1. Newman, M.E., The structure and function of complex networks. SIAM review, 2003. 45(2): p. 167-256.
2. Ellison, N.B., Social network sites: Definition, history, and scholarship. Journal of Computer‐Mediated Communication, 2007. 13(1): p. 210-230.
3. Xu, T., et al., Generative models for evolutionary clustering. ACM Transactions on Knowledge Discovery from Data (TKDD), 2012. 6(2): p. 7.
4. Barber, M.J., Modularity and community detection in bipartite networks. Physical Review E, 2007. 76(6): p. 066102.
5. Plantié, M. and M. Crampes, Survey on social community detection, in Social media retrieval. 2013, Springer. p. 65-85.
6. Bagrow, J.P. and E.M. Bollt, Local method for detecting communities. Physical Review E, 2005. 72(4): p. 046108.
7. Girvan, M. and M.E. Newman, Community structure in social and biological networks. Proceedings of the national academy of sciences, 2002. 99(12): p. 7821-7826.
8. Clauset, A., M.E. Newman, and C. Moore, Finding community structure in very large networks. Physical review E, 2004. 70(6): p. 066111.
9. Zhao, Y., Jiang, W., Li, S., Ma, Y., Su, G., & Lin, X. (2015). A cellular learning automata based algorithm for detecting community structure in complex networks. Neurocomputing, 151, 1216- 1226
10. Panizo, A., G. Bello-Orgaz, and D. Camacho. A genetic algorithm with local search based on label propagation for detecting dynamic communities. in Intelligent Distributed Computing XII. 2018. Springer.
11. Jokar, E., M. Mosleh, and M. Kheyrandish, GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks. The Journal of Supercomputing, 2022. 78(5): p. 7354-7377.
12. F. Basharatnia, A. Talebpour, and S. Ali Akbari, Community detection in static social networks using gray wolf optimization algorithm. Bi-quarterly Journal of Soft Engineering and Computer Engineering/Vol. 4, No. 1, Spring and Summer 1. 2017
13. Raghavan, U.N., R. Albert, and S. Kumara, Near linear time algorithm to detect community structures in large-scale networks. Physical review E, 2007. 76(3): p. 036106.
14. ZhuЃ, X. and Z. GhahramaniЃн, Learning from labeled and unlabeled data with label propagation. 2002.
15. S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014.
16. Newman, M.E. and M. Girvan, Finding and evaluating community structure in networks. Physical review E, 2002. 69(2): p. 026113.

17. Lusseau D. The emergent properties of a dolphin social network[J]. Proceedings of the Royal Society of London. Series B: Biological Sciences, 2003, 270(Suppl 2): S186- S188.
18. A. Mohammadzadeh, et al., Presenting an improved gray wolf optimization algorithm for Workflow scheduling in cloud computing environment, Scientific Research Journal of Soft Computing and Information Technology, Vol. 8, No. 4, 2018
19. Jian Zhou, Yuxin Chen, Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method, Frontiers in PublicHealth, 2023,
20. Maliheh Ghasemzadeh, Mohsen Ashourian،Identifying Communities on Static Social Networks، Majlesi Journal of Telecommunication Devices، Vol. 8, No. 3, September 2019.