Online social networks such as Facebook, Twitter, Instagram, and LinkedIn have provided an appropriate platform for people to interact with each other and disseminate different types of information. Thus, analyzing these networks is increasingly important for discovering behavior patterns of interactions among individuals and evolution of the networks over time, as well as developing algorithms required for meaningful analysis. Due to uncertain, dynamic and time-varying nature of social interactions in online social networks, especially in activity and interaction networks, some properties of networks such as network centralities, trust values, diffusion probabilities and user influences change dynamicity over time. Therefore, it would be difficult to capture the structural and dynamical properties of the network. To deal with this problem, several studies based on learning systems have been presented in the literature to reflect dynamical behavior of social network issues in time. In recent years, learning automaton (LA) as a promising intelligent technique has presented potential solutions for many real network problems and has the advantage of being able to work in unknown, uncertain, complex and dynamic environments. This book is aimed to survey recent developments in problems of social networks addressed by learning automata theories, which are related to network measures, network sampling, stochastic networks, stochastic graphs, community detection, link prediction, trust management, recommender system, influence maximization and their applications.