using an evaluator fixed structure learning automata in sampling of social networks

Social networks are streaming, and diverse including a wide range of edges so that they are continuously evolved over time and are formed by the activities such as tweets and emails among the users; each activity, adds an edge to the network graph. despite their popularity, the dynamically and large size of most social networks make it difficult or impossible to study the entire networks. this paper proposes a sampling algorithm that is equipped with an evaluator unit for analyzing the edges and a set of simple fixed structure learning automata. the evaluator unit evaluates each edge and then decides whether the edge and the 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 developed algorithm is compared with the best current sampling algorithm reported in the kolmogorov-smirnov test, and the normalized l1 and l2 distances in real networks and synthetic networks are presented as a sequence of edges. the experimental results obtained show the superiority of the proposed algorithm.