A simulation method for social networks

Authors: Rui Zeng Quan Z Sheng Lina Yao

Publish Date: 2015/02/05

Volume: 5, Issue: 1, Pages: 6-

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Abstract

With the increasing popularity of social networks it is becoming more and more crucial for the decision makers to analyze and understand the evolution of these networks to identify for example potential business opportunities Unfortunately understanding social networks which are typically complex and dynamic is not an easy task In this paper we propose an effective and practical approach for simulating social networks We first develop a social network model that considers growth and connection mechanisms including addition and deletion of social networks We consider the nodes’ indegree internodes’ close degree which indicates how close the nodes are in the social network which is limited by the indegree threshold We then develop a graphbased stratified random sampling algorithm for generating an initial network To obtain the snapshots of a social network of the past current and the future we further develop a close degree algorithm and a close degree of estimation algorithm The degree distribution of our model follows a powerlaw distribution with a “fattail” Experimental results using reallife social networks show the effectiveness of our proposed simulation method

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