using the gray wolf optimization algorithm for community detection

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.