Link Prediction in Social Networks through classifiers combination

Link prediction in social networks is one of the most important activities in analysis of such networks. The importance of link prediction in social networks is due to its dynamic nature. While members and their relationships (links) in such networks are continuously increasing, links may be missed due to various reasons. By predicting such links, the possibility of extension, completion, and information retrieval of these networks can be provided. For predicting and detecting these links, some information is needed, which they could be extracted from the network graph structure and used as measures for prediction. Having introduced two new measures, which lead to an effective performance in predictions and recommendations, we propose a new method. The link prediction is achieved through combining classifiers and evolutionary methods of GA (Genetic Algorithm) and ICA (Imperialist Competitive Algorithm). To show practical considerations, two real datasets, Facebook and Epinions, are employed. We show that the proposed method can increase the performance and precision of prediction.