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40 evidence of sub-linear preferential attachment. Our work shares similar goals and methodology as the above studies. However, the data sets we use are orders of magnitude larger than the ones used before. Moreover, our data allows us to analyze network growth over a large number of samples. We analyze daily snapshots of Flickr and YouTube networks, and weekly snapshots of the Internet topology. For Wikipedia, we have sufficient data to create a snapshot of the network at the precise second a new link is established. Since the growth models rely solely on the current network structure to predict new link formation, having frequent snapshots of the network is crucial to validating the models with high accuracy. Researchers have also studied the high-level properties of graph evolution, looking for evolution trends at the global level. For instance, Leskovec et al. [87] examined the evolution of a number of real-world graphs, including collaboration networks and recommendation networks. They found that the graphs tend to densify, and that the average path length tends to shrink (instead of growing in proportion to the number of nodes). Additionally, Kumar et al. [81] observed the early evolution of the blogosphere, and found that it is rapidly increasing in both scale and connectedness. This line of work is largely complementary to our work, as we focus on the local link formation phenomena which might lead to these global observations.PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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