Link prediction in weighted social networks using learning automata

Link prediction is an important task in Social Network Analysis. The present paper addresses predicting the emergence of future relationships among nodes in a social network. Our study focuses on a strategy of learning automata for link prediction in weighted social networks. In this paper, we try to estimate the weight of each test link directly from the weights information in the network. To do so, we take advantage of using learning automata, intelligent tools that try to learn the optimal action based on reinforcement signals. In the method proposed here, there exist one learning automata for each test link that must be predicted and each learning automata tries to learn the true weight of the corresponding link based on the weight of links in the current network. All learning automata iteratively select their action as the weight of corresponding links. The set of learning automata actions will then be used to calculate the weight of training links and each learning automata will be rewarded or punished according to its influence upon the true weight estimating of the training set. A final prediction is then performed based on the estimated weights. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when weights are considered.