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Chapter 6 Network Growth To date, most measurement and analysis of online social networks (including the preceding chapter) has focused on the properties of static network snapshots. Despite the different goals and purposes of the various online social networking sites, the underlying social networks have been shown to exhibit a surprising number of common structural features, such as a highly skewed (power-law) degree distribution, a small diameter, and significant local clustering [6,105]. This intriguing similarity suggests that the same underlying network growth processes may be at play in the different sites. A proper understanding of these growth processes can provide insights into the observed network structure, allow predictions of future network growth, and enable simulation of systems on social networks of arbitrary size. However, most work on growth processes for large-scale networks has focused on theoretical models, instead of deriving the growth properties from empirical data. For example, two of the popular theoretical growth models are the Barab ́asi-Albert model [15], where users connect to other users in proportion to the destination’s popularity, and the random walk model [142,154], where users connect to other users who are already close in the network. 102PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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