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indicates that the graph has scale-free properties. 5.9 Groups In many online social networks, users with shared interests may create and join groups. Table 5.5 shows the high-level statistics of user groups in the four networks we study. Participation in user groups varies significantly across the different networks: only 8% of YouTube users but 61% of LiveJournal users declare group affiliations. Once again, the group sizes follow a power-law distribution, in which the vast majority have only a few users each. 96 Network Flickr LiveJournal Orkut YouTube Groups 103,648 7,489,073 8,730,859 30,087 Usage Average Size 21% 82 61% 15 13% 37 8% 10 Average C 0.47 0.81 0.52 0.34 Table 5.5 : Table of the high-level properties of network groups including the fraction of users which use group features, average group size, and average group clustering coefficient. Note that users in a group need not necessarily link to each other in the social network graph. As it turns out, however, user groups represent tightly clustered communities of users in the social network. This can be seen from the average group clustering coefficients of group members, shown in Table 5.5.6 These coefficients are 6We define the group clustering coefficient of a group G as the clustering coefficient of the subgraphPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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