Unbiased sampling in directed social graph

Authors:
Tianyi Wang;Yang Chen;Zengbin Zhang;Peng Sun;Beixing Deng;Xing Li
Affiliations:
Tsinghua University, Beijing, China;University of Goettingen, Goettingen, Germany;University of California, Santa Barbara, Santa Barbara, CA, USA;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the ACM SIGCOMM 2010 conference
Year:
2010

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Abstract

Microblogging services, such as Twitter, are among the most important online social networks(OSNs). Different from OSNs such as Facebook, the topology of microblogging service is a directed graph instead of an undirected graph. Recently, due to the explosive increase of population size, graph sampling has started to play a critical role in measurement and characterization studies of such OSNs. However, previous studies have only focused on the unbiased sampling of undirected social graphs. In this paper, we study the unbiased sampling algorithm for directed social graphs. Based on the traditional Metropolis-Hasting Random Walk (MHRW) algorithm, we propose an unbiased sampling method for directed social graphs(USDSG). Using this method, we get the first, to the best of our knowledge, unbiased sample of directed social graphs. Through extensive experiments comparing with the "ground truth" (UNI, obtained through uniform sampling of directed graph nodes), we show that our method can achieve excellent performance in directed graph sampling and the error to UNI is less than 10%.