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In this thesis, we focus only on structural growth models. Validation of growth models It is important to note here that both structural and explanatory growth models are, by and large, intuitive models that can explain the observed structural properties of the networks. But, they have not been significantly validated using empirical data. Mitzenmacher [107] poses this as one of the biggest challenges facing the future of power-law research. One of the contributions of this thesis lies in collecting data that can be used to determine how well these processes predict what actually occurs in different real-world networks at scale. 3.2.2 Observations of network growth With the growth in popularity of online social networks, a few studies have examined the properties of the networks over time. We briefly describe these studies below. A few studies have looked at how links are formed in social networks. Kossinets and Watts [80] used an inferred social network from an email trace to show that new links in the network are more likely to be established between nodes close to each other. Nowell et al. [93] investigated co-authorship networks in physics to test how well different graph proximity metrics can predict future collaborations. New- man [114] and Jeong et al. [69] examined the properties of scientific collaboration networks and found evidence of preferential attachment. Peltom ̈aki and Alava [126] examined a scientific collaboration network and a movie-actor network and found 39PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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