Introduction to algorithms
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Characterizing privacy in online social networks
Proceedings of the first workshop on Online social networks
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Are friends overrated? A study for the social news aggregator Digg.com
Computer Communications
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Crawling and detecting community structure in online social networks using local information
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
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While enabling new research questions and methodologies, the massive size of social media platforms also poses a significant issue for the analysis of these networks. In order to deal with this data volume, researchers typically turn to samples of these graph structures to conduct their analysis. This however raises the question about the representativeness of such limited crawls, and the amount of data necessary to come to stable predictions about the underlying systems. This paper analyzes the convergence of six commonly used topological metrics as a function of the crawling method and sample size used. We find that graph crawling methods drastically over- and underestimate network metrics, and that a non-trivial amount of data is needed to arrive at a stable estimate of the underlying network.