Providing a new method for link prediction in social networks based on the meta-heuristic algorithm

Abstract

Social network analysis is one of the most important research fields in data mining today. The purpose of the analysis of these networks is to extract the embedded knowledge in the data set and to learn the behavior of users in the social networking environment. One of the most attractive and central applications of social network analysis is link prediction. The purpose of link prediction in social networks is to identify missing and unknown information from users or to predict the future link between two users. In recent years, various artificial intelligence algorithms have been introduced as one of the most important tools for resolving link prediction and big data. In this research, a strategy based on a meta-heuristic is used to improve link prediction in social networks. The proposed method is based on the characteristics of the signed social networks provided and turns the link prediction problem into a two-class classification problem. Then uses the capability of the Particle Swarm Optimization (PSO) and the topological properties of the social network graph to create a database with two classes, the first class pointing to the existence of a connection between the users and the second class pointing to the absence of this relationship. Creates a database using the support vector machine model for categorization work and uses the classic Katz similarity criterion for end-user suggestions. Twitter social network information has been used to compare and evaluate the proposed method. The results of the experiments show the superiority of the proposed method with 0.23, 0.99, and 6.32, respectively, compared to Meta-Path, Katz and CN algorithms in F-measure criterion.