Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm

Nowadays, social networks play an important role in people's daily lives. A social network is a kind of social structure that consists of several nodes that can be individuals or organizations. Most importantly, these nodes are connected by one or more specific types of dependencies, such as friendships or work relationships. Understanding the structure and constituent groups of these networks can give us useful information about the state of society and individuals. In this article, a new solution to solve the problem of social structure detection is provided. Social structure means communities or associations in social networks. An important issue of this context is network graph construction based on objects as nodes and edges as transactions between these objects. Community detection is based on these graphs. The appropriate solution is to identify and create clusters of nodes that have strong connections with each other and at the same time have weaker connections between nodes of different clusters. Optimization algorithms can be used to construct and detect these connections. Harmonic search is one of the efficient optimization algorithms in this context. However, in the field of identifying communication structures and communities, so far, no research work has been done using the harmonic search algorithm. In this paper, a new method is proposed to construct network clusters based on network graphs. It can identify effective communications based on different criteria. In order to propose this method, first a new version of harmonic search algorithm is designed for discrete environments while the original version of which is for continuous environments. Then, according the problem, which is to discover appropriate structures in the social network graph, a new method is devised to solve it. This method tries to provide a suitable discrete version by relying on different operators to be applicable to solve the desired problem. In order to evaluate the proposed method, various experiments were carried out on several different networks. These networks have been used as benchmark in previous research work. For evaluation and comparison, two artificial networks and two real networks are considered. The evaluation results of the proposed method on these networks are presented based on different criteria and compared with four previous algorithms that are known in this field. Comparison results show that the proposed algorithm is relatively superior to other algorithms or at least produces similar results. The most important reason that can justify the relative performance superiority of the proposed algorithm or at least its competitive results is the better search capability of the problem search space. This leads to the discovery of more promising points and the production of better solutions.