Preprint submitted to International Journal of Communication Systems A new learning automata based sampling algorithm for social networks Alireza Rezvanian and Mohammad Reza Meybodi Soft Computing Laboratory, Computer Engineering and Information Technology Department Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran SUMMARY Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information and social networks. In this paper, a sampling algorithm for complex social networks which is based on a new version of distributed learning automata (DLA) reported recently called extended distributed learning automata (eDLA) is proposed. For evaluation purpose, the eDLA based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for number of well-known sampling algorithms in terms of relative error (RE) and Kolmogorov-Smirnov (KS) test. It is shown that eDLA based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA based sampling algorithm in comparison with the DLA based sampling algorithm has a 26.93% improvement for the average of KS value for degree distribution taken over all test networks. KEY WORDS: Complex social networks; social network analysis; network sampling; learning automata; extended distributed learning automata. 1. INTRODUCTION Todays, online social networks (OSN) provide a suitable platform for sharing and broadcasting a variety of information and multimedia by users. Due to the various applications of OSN among researchers from the study the characterization of the network to that of financial applications, studying and analyzing of the social networks arise new challenging research topics in the literature [1–4]. Online social networks have attracted widespread of attention in the existing network researches. Some researchers tried to analyzing the real network such as social network by observing structures and properties of network and then generate a model to reveal their properties. For example, many of real networks reflect universal properties such as small-world property, which indicates that most of real networks have a short average distance between any pair of nodes in a real-world networks [5]. Another famous phenomenon for real-world networks is scale-free property with a power law degree distribution, which implies that several nodes on these networks as known hubs have a very large number of edges in comparison with other nodes abundantly [6]. Furthermore, most of real-world network such as social networks is large, dynamic, complex, and also due to privacy settings of online social networks some part of online social networks is not fully available [7]. Thus, in the most of the time, it is impossible to have a direct access on these networks to study and analyze their characterization and properties. Therefore, researchers try to study and analyze real networks such as communication, technological, information and online social networks via some metrics (i.e.,