Unbiased sampling in directed social graph

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%.

Estimating PageRank deviations in crawled graphs

“…The other area of related work comprises of graph sampling approaches which can be broadly classified into two categories: traversal based methods (Leskovec and Faloutsos 2006;Wang et al 2010;Maiya and Berger-Wolf 2011) and random walk based methods (Gjoka et al 2011;Li et al 2015;Hübler et al 2008). Graph-traversal based methods employ breadth-first search (BFS) or the depth-first search (DFS) algorithm to sample vertices and are typically shown to exhibit bias towards high-degree vertices (Wang et al 2010). Maiya and Berger-Wolf (2011) compare various traversal based algorithms and define representativeness of a sample while proposing how to guide the sampling process towards inclusion of desired properties.…”