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4.2 Capturing social networks’ structure We now discuss our methodology for crawling each of the networks, their limitations, and high-level statistics of the resulting data sets. Using automated scripts on a cluster of 58 machines, we crawled the social network graphs of Flickr, LiveJournal, Orkut, and YouTube. We chose these four sites because they are among the most popular social networking sites and they allow us to view the links out of any user in the network. In each step of our crawls, we retrieved the list of friends for a user we had not yet visited and added the retrieved users to the list of users to visit. We continued until we exhausted the list. This corresponds to a BFS of the social network graphs. High-level statistics of the resulting data sets are presented in Table 5.1. Since the focus of this part is to investigate the structure of online social networks, we focus on the large WCC of the corresponding graphs in the rest of this thesis. As we show later in this section, the large WCC is structurally the most “interesting” part of the network. The nodes not included in the large WCC tend to be either part of very small, isolated clusters or are not connected to other users at all. 4.2.1 Flickr Flickr (www.flickr.com) is a photo-sharing site based on a social network. The Flickr data presented in this thesis is from a crawl of the large WCC conducted on January 9th, 2007, and contains over 1.8 million users and 22 million links. Flickr exports an API for third-party developers, and we used this API to conduct the crawl. We also 60PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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