Sara YounessZadeh - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin,Iran
Mohammad Reza Meybodi - Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran

Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electronic commerce and recommender systems or identification of terroristic relations in social networks. In this article, a new idea is presented for the prediction. It is an integration of the two methods of prediction of similarity score based link and prediction of probabilistic link, which is placed in a new category of link prediction methods. This idea acquires the similarity score between nodes from probabilistic techniques and through using learning automata, and provides better results compared to other criteria methods on standard datasets.

Distributed Learning Automata, Similarity Score, Social Networks Analysis, Link Prediction, Online Social Networks