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

TL;DR

This paper develops and evaluates methods for obtaining uniform samples of users in online social networks, demonstrating techniques like MHRW and RWRW, and applying them to Facebook to analyze its user properties.

Contribution

It introduces practical sampling frameworks with convergence diagnostics for OSNs and provides the first representative Facebook user sample for analysis.

Findings

MHRW and RWRW produce approximately uniform samples

BFS and unadjusted RW lead to biased results

Online diagnostics effectively assess sample quality

Abstract

Our 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, we 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 we demonstrate through a comparison to each other as well as to the "ground truth." In contrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW) leads to substantially biased results. Second, and 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 diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality. Third, as a case study, we apply the above methods to Facebook and we collect the first, to the best of our knowledge, representative sample of Facebook users. We make it publicly available and employ it to characterize several key properties of Facebook.