Nature inspired link prediction and community detection algorithms for social networks: a survey

Social network analysis (SNA) has become a prominent research area in recent times. The popularity increased due to the rich information these networks possess. SNA is a domain of data analytics that practices graph theory to understand the social structures.Understanding and Analyzing the present links in the social network to predict the future possible links from the existing.The Network forms the interesting link problem and finding the similar groups in networks which is considered as the Community detection problem.The wide variety of applications of link prediction and community detection problems are recommendations.suggesting friends to users, predict the criminal association,inferences in biology networks and to analyze the trends especially in marketing. In various fields of science, the complexity of optimization problems increases as technology progresses. To find an optimum solution to a problem, there exists several approaches. In this regard swarm optimization techniques are more prominent. In SNA use of swarm optimization techniques has been employed in many aspects. Among these, the community detection and link prediction in SNA, is a key problem. Since with the growing network to find the similarity between the nodes in the network is a time consuming process to optimize the process many researchers using nature inspired algorithms to solve the link prediction and community detection problems. Apart from these problems nature inspired algorithms are widely used in many fields to solve constraint based optimization problems. In this paper, a review on swarm optimization techniques namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABC) and Firefly Algorithm (FA) along with its application in community detection have made in detail. A qualitative comparison is made among these methods to analyze and infer the nature of parameters involved in approaching an optimal solution.