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95 ment also have no locality bias, as preferential attachment is a global process, and they provide a point of reference to the clustering in a graph with a similar degree distribution. The clustering coefficients of social networks are between three and five orders of magnitude larger than their corresponding random graphs, and about one order of magnitude larger than random power-law graphs. This unusually high clustering coefficient suggests the presence of strong local clustering, and has a natural expla- nation in social networks: people tend to be introduced to other people via mutual friends, increasing the probability that two friends of a single user are also friends. 0.5 0.4 0.3 0.2 0.1 0 YouTube LiveJournal Flickr Orkut 1 10 100 1000 Outdegree 10000 Figure 5.9 : Clustering coefficient of users with different outdegrees. The users with few “friends” are tightly clustered. Figure 5.9 shows how the clustering coefficients of nodes vary with node outdegree. The clustering coefficient is higher for nodes of low degree, suggesting that there is significant clustering among low-degree nodes. This clustering and the small diameter of these networks qualifies these graphs as small-world networks [159], and further Avg. Clustering CoefficientPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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