Shahrood University of Technology Journal of AI and Data Mining 2322-5211 8 1 2020 01 01 Using an Evaluator Fixed Structure Learning Automata in Sampling of Social Networks 127 148 1614 10.22044/jadm.2019.7145.1842 EN S. Roohollahi Computer Engineering Department, Kerman Branch, Islamic Azad University, Kerman, Iran. A. Khatibi Bardsiri Computer Engineering Department, Kerman Branch, Islamic Azad University, Kerman, Iran. F. Keynia Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences; Graduate University of Advanced Technology, Kerman, Iran. Journal Article 2018 06 06 Social networks are streaming, diverse and include a wide range of edges so that continuously evolves over time and formed by the activities among users (such as tweets, emails, etc.), where each activity among its users, adds an edge to the network graph. Despite their popularities, the dynamicity and large size of most social networks make it difficult or impossible to study the entire network. This paper proposes a sampling algorithm that equipped with an evaluator unit for analyzing the edges and a set of simple fixed structure learning automata. Evaluator unit evaluates each edge and then decides whether edge and corresponding node should be added to the sample set. In The proposed algorithm, each main activity graph node is equipped with a simple learning automaton. The proposed algorithm is compared with the best current sampling algorithm that was reported in the Kolmogorov-Smirnov test (KS) and normalized L1 and L2 distances in real networks and synthetic networks presented as a sequence of edges. Experimental results show the superiority of the proposed algorithm. https://jad.shahroodut.ac.ir/article_1614_3d0f4de9c471b189ab83b63cafda6650.pdf