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on crawling this component of the graph. 4.1.1 Crawling the entire large WCC The primary challenge in crawling large graphs is covering the entire giant connected component. At each step, one can generally only obtain the set of links into or out of a specified node. In the case of online social networks, crawling the graph efficiently is important since the graphs are large and highly dynamic. Common algorithms for crawling graphs include breadth-first search (BFS) and depth-first search. Often, crawling the entire giant connected component is not feasible, and one must resort to using samples of the graph. Crawling only a subset of a graph by ending a BFS early (called the snowball method) is known to produce a biased sample of nodes [85]. In particular, partial BFS crawls are likely to overestimate node degree and underestimate the level of symmetry [18]. In social network graphs, collecting samples via the snowball method has been shown to underestimate the power-law coefficient, but to more closely match other metrics, including the overall clustering coefficient [85]. However, some previous studies of social networks have used small graph samples. Example studies have used samples of 0.3% of Orkut users [6], less than 1% of LiveJournal communities [12], and 0.08% of MySpace users [6]. In this thesis, we obtain and study much larger samples of the user graphs. 58PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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