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A new learning automata‐based sampling algorithm for social networks
Author(s) -
Rezvanian Alireza,
Meybodi Mohammad Reza
Publication year - 2015
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3091
Subject(s) - computer science , sampling (signal processing) , algorithm , social network (sociolinguistics) , learning automata , theoretical computer science , artificial intelligence , machine learning , automaton , social media , filter (signal processing) , world wide web , computer vision
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 the 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 that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA ( e DLA) is proposed. For evaluation purpose, the e DLA‐based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well‐known sampling algorithms in terms of relative error and Kolmogorov–Smirnov test. It is shown that e DLA‐based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the e DLA‐based sampling algorithm in comparison with the DLA‐based sampling algorithm has a 26.93% improvement for the average of Kolmogorov–Smirnov value for degree distribution taken over all test networks. Copyright © 2015 John Wiley & Sons, Ltd.