Toward cost-efficient sampling methods

Peng Luo (), Yongli Li (), Chong Wu () and Guijie Zhang ()
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Peng Luo: School of Management, Harbin Institute of Technology, Harbin 150001, P. R. China;
Yongli Li: School of Management, Harbin Institute of Technology, Harbin 150001, P. R. China;
Chong Wu: School of Management, Harbin Institute of Technology, Harbin 150001, P. R. China
Guijie Zhang: School of Management, Harbin Institute of Technology, Harbin 150001, P. R. China

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 05, 1-16

Abstract: The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper proposes two new sampling methods based on the idea that a small part of vertices with high node degree could possess the most structure information of a complex network. The two proposed sampling methods are efficient in sampling high degree nodes so that they would be useful even if the sampling rate is low, which means cost-efficient. The first new sampling method is developed on the basis of the widely used stratified random sampling (SRS) method and the second one improves the famous snowball sampling (SBS) method. In order to demonstrate the validity and accuracy of two new sampling methods, we compare them with the existing sampling methods in three commonly used simulation networks that are scale-free network, random network, small-world network, and also in two real networks. The experimental results illustrate that the two proposed sampling methods perform much better than the existing sampling methods in terms of achieving the true network structure characteristics reflected by clustering coefficient, Bonacich centrality and average path length, especially when the sampling rate is low.

Keywords: Sampling method; complex network; network sampling; statistical physics; simulation; 02.10.Ox; 02.60.Cb (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0129183115500503

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