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103 In this chapter, we use network growth data from two online social networks and two other real-world networks to validate existing models of network growth. In particular, we study how well the empirical data matches the predictions of growth models that have been proposed. As before, we compare our results to those for other, well-understood networks in order to ground our analysis. However, we are well aware that the user graph in social networks is fundamentally different from the interconnection of web pages or the connections between autonomous systems in the Internet. It is important to note that we can only study how well a particular model predicts the link creation that occurs in the empirical data. We fundamentally do not know why new links were established; we can only observe the source and destination of new links. Thus, we cannot ultimately prove or disprove any particular model; we can only examine the correlation between the observed data and what each model would predict. Nevertheless, knowing how well different models predict link creation in the data can improve our understanding of network evolution, and can provide clues as to the actual underlying processes. 6.1 High-level data characteristics Table 6.1 shows the high-level statistics of the data we gathered in order to study the growth of large networks at scale. The network sizes vary by over three orders of magnitude. Similarly, other metrics, such as the average number of links per nodePDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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