An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks

The structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering of the network, and the community separates a graph. In recent years, public methods suffer from inefficiency because of the high complexity of time and the need for full access to graph information. In contrast, smart methods such as meta-heuristic algorithms, the use of low parameters and much less complex time complexity have been among the most popular methods in recent years. These methods have good features, but they still face problems such as dependence on finding the best point in search space, global updates, and poor quality due to the formation of large communities and others. In this paper, in order to improve the mentioned problems, a method is proposed based on combining the Firefly Algorithm (FA) and Learning Automata (LA). In the proposed model, LA is used to increase the efficiency of the FA. Choosing the best neighbours for the FA agents is done using the LA. The results from the four datasets of Karate, Dolphins, Polbooks, and Football show that the proposed model has more Normalized Mutual Information (NMI) than other models.