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81 butions. However, as our analysis shows, the degree distributions in social networks differ from that of other power-law networks in several ways. 11 0.01 0.0001 1e-06 1e-08 1 0.01 0.0001 1e-06 1e-08 100 Outdegree (a) Flickr 10000 1 100 10000 Outdegree (b) LiveJournal 11 0.01 0.0001 1e-06 1e-08 1 0.01 0.0001 1e-06 1e-08 100 10000 Outdegree (c) Orkut 1 100 10000 Outdegree (d) YouTube Figure 5.1 : Log-log plot of outdegree complementary cumulative distribution func- tions (CCDF). All social networks show properties consistent with power-law net- works. Figures 5.1 and 5.2 shows the outdegree and indegree complementary cumulative distribution functions (CCDF), respectively, for each measured social network. All of the networks show behavior consistent with a power-law network; the majority of the nodes have small degree, and a few nodes have significantly higher degree. To test how well the degree distributions are modeled by a power-law, we calculated the Frequency Frequency Frequency FrequencyPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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