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Our goal in this paper is to develop a practical framework for obtaining auniform sample of users in an online social network (OSN) by crawling itssocial graph. Such a sample allows to estimate any user property and sometopological properties as well. To this end, first, we consider and compareseveral candidate crawling techniques. Two approaches that can produceapproximately uniform samples are the Metropolis-Hasting random walk (MHRW) anda re-weighted random walk (RWRW). Both have pros and cons, which we demonstratethrough a comparison to each other as well as to the "ground truth." Incontrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW)leads to substantially biased results. Second, and in addition to offlineperformance assessment, we introduce online formal convergence diagnostics toassess sample quality during the data collection process. We show how thesediagnostics can be used to effectively determine when a random walk sample isof adequate size and quality. Third, as a case study, we apply the abovemethods to Facebook and we collect the first, to the best of our knowledge,representative sample of Facebook users. We make it publicly available andemploy it to characterize several key properties of Facebook.

A Walk in Facebook: Uniform Sampling of Users in Online Social Networks