A new learning automata-based sampling algorithm for social networks

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.