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The authors' goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an Online Social Network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, they consider and compare several candidate crawling techniques. Two approaches that can produce approximately uniform samples are the Metropolis-Hasting Random Walk (MHRW) and a Re-Weighted Random Walk (RWRW). Both have pros and cons, which they demonstrate through a comparison to each other as well as to the "Ground truth."
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