Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

With more than 250 million active users, Facebook (FB) is currently one of the most important online social networks. Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph. In this quest, we consider and implement several candidate techniques. Two approaches that are found to perform well are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground-truth" (UNI - obtained through true uniform sampling of FB userIDs). In contrast, the traditional Breadth-First-Search (BFS) and Random Walk (RW) perform quite poorly, producing substantially biased results. In addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these can be used to effectively determine when a random walk samp...

• Type: Conference
• Year: 2010
• Where: INFOCOM
• Authors: Minas Gjoka, Maciej Kurant, Carter T. Butts, Athina Markopoulou
• Keywords: Social Sampling Online Networks Measurements Graph Facebook Crawling Bias