Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109477
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Type: Journal article
Title: A simulation method for social networks
Author: Zeng, R.
Sheng, Q.
Yao, L.
Citation: Social Network Analysis and Mining, 2015; 5(1):1-14
Publisher: Springer
Issue Date: 2015
ISSN: 1869-5450
1869-5469
Statement of
Responsibility: 
Rui Zeng, Quan Z. Sheng, Lina Yao
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’ in-degree, inter-nodes’ close degree which indicates how close the nodes are in the social network, which is limited by the in-degree threshold. We then develop a graph-based 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 power-law distribution with a “fat-tail”. Experimental results using real-life social networks show the effectiveness of our proposed simulation method.
Keywords: Social network; simulation; adjacent matrix; power-law distribution; in-degree; close degree
Rights: © Springer-Verlag Wien 2015
RMID: 0030040045
DOI: 10.1007/s13278-015-0246-4
Appears in Collections:Computer Science publications

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