An improved sampling method of complex network

Authors: 
Qi Gao, School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, P. R. China
Xintong Ding, School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, P. R. China
Feng Pan, Corresponding author, School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, P. R. China
Weixing Li, School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, P. R. China
Abstract: 

Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

Received: 
Sunday, June 30, 2013
Accepted: 
Monday, September 30, 2013
Published: 
Wednesday, December 4, 2013
keywords: 
Complex network
sampling method
subnet
PACS: 
11.25.Hf
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