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Practical recommendations on crawling online social networks (2011)
| Venue: | SELECTED AREAS IN COMMUNICATIONS, IEEE JOURNAL ON |
| Citations: | 37 - 1 self |
BibTeX
@ARTICLE{Gjoka11practicalrecommendations,
author = {Minas Gjoka and Maciej Kurant and Carter T. Butts and Athina Markopoulou},
title = {Practical recommendations on crawling online social networks},
journal = {SELECTED AREAS IN COMMUNICATIONS, IEEE JOURNAL ON},
year = {2011},
pages = {2011}
}
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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.
Keyphrases
online social network practical recommendation uniform sample several key property social graph user property case study adequate size re-weighted random walk representative sample topological property ground truth facebook user metropolis-hasting random walk online formal convergence diagnostics biased result unadjusted random walk practical framework several candidate random walk sample data collection process performance assessment sample quality
