Title: SGP: a social network sampling method based on graph partition

Authors: Xiaolin Du; Dan Wang; Yunming Ye; Yan Li; Yueping Li

Addresses: Department of Computer Science, Beijing University of Technology, Beijing, China ' Department of Computer Science, Beijing University of Technology, Beijing, China ' Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China ' School of Computer Engineering, Shenzhen Polytechnic, Shenzhen, China ' School of Computer Engineering, Shenzhen Polytechnic, Shenzhen, China

Abstract: A representative sample of a social network is essential for many internet services that rely on accurate analysis. A good sampling method for social network should be able to generate small sample network with similar structures and distributions as its original network. In this paper, a sampling algorithm based on graph partition, sampling based on graph partition (SGP), is proposed to sample social networks. SGP firstly partitions the original network into several sub-networks, and then samples in each sub-network evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. Finally, we evaluate SGP on several well-known datasets. The experimental results show that SGP method outperforms seven state-of-the-art methods.

Keywords: sampling algorithms; social networks; graph partition; community structure; topology structure.

DOI: 10.1504/IJITM.2019.099809

International Journal of Information Technology and Management, 2019 Vol.18 No.2/3, pp.227 - 242

Accepted: 07 Nov 2016
Published online: 10 May 2019 *

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