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Understanding Graph Sampling Algorithms for Social Network Analysis
Citation key WCZXJHDL-UGSASNA-11
Author Wang, Tianyi and Chen, Yang and Zhang, Zengbin and Xu, Tianying and Jin, Long and Hui, Pan and Deng, Beixing and Li, Xi
Title of Book Proceedings of Annual Workshop on Simplifying Complex Networks for Practitioners (Simplex '11), co-located with ICDCS '11
Pages 123–128
Year 2011
ISBN 978-0-7695-4386-4
ISSN 1545-0678
DOI http://dx.doi.org/10.1109/ICDCSW.2011.34
Location Minneapolis, MN, USA
Address New York, NY, USA
Month June
Publisher IEEE
Abstract Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for social network analysis. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies, but there lacks fair evaluation and comparison among them. In this paper, we analyze the state-of-art graph sampling algorithms and evaluate their performance on some widely recognized graph properties on directed graphs using large-scale social network datasets. We evaluate not only the commonly used node degree distribution, but also clustering coefficient, which quantifies how well connected are the neighbors of a node in a graph. Through the comparison we have found that none of the algorithms is able to obtain satisfied sampling results in both of these properties, and the performance of each algorithm differs much in different kinds of datasets.
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