A novel bi-objective model with particle swarm optimizer for structural balance analytics in social networks

Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of the algorithm can yield a set of Pareto solutions, each of which represents a certain network partition that divides a signed network into many clusters. Consequently, by simultaneously optimizing the objectives in the proposed model, one may have many choices to analyze the balance problem. Extensive experiments compared against several other models and algorithms have been done. All the experiments indicate that the proposed model is helpful for social network structural balance analytics, and that the algorithm is effective.