Dynamic Community in static social networks using the gray wolf optimizer algorithm

Identifying communities in complex networks is an important issues in social network analysis, and it helps researchers understand the function and display of network structures. Clustering or recognizing communities will reveal the structure of groups in social networks and hidden communication between its components. A community is a collection of nodes whose density of communication is more than the other network entities.In this paper, a new algorithm for recognizing communities in static networks has been presented which utilizes Gray Wolf Optimizer algorithm, which has the ability to scale according to the selected criteria. It has been shown that one of the most important characteristics of meta-algorithms is the lack of trapping at the local minimum. Gray Wolf Optimizer algorithm is less likely to be trapped than other optimization algorithms such as the genetic algorithm and the particle swarm algorithm. Finally, the results of the experiments showed that the algorithm is better than other algorithms on average.