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6 Chapter 6, we use empirical data to understand the growth processes that lead to the observed network structure. Our analysis of large-scale growth data shows that new links are created and received by users in direct proportion to their current number of links, and that users tend to quickly respond to incoming links by creating a link in the reverse direction. Additionally, our analysis reveals a strong proximity bias when users select other users to link to: users tend to connect to nearby users in the network much more often than would be expected from previously proposed growth models. Our work on studying network growth is an important first step towards un- derstanding the processes that shape the structure of online social networks. Our work enables the creation of synthetic networks that reflect both global and local characteristics of online social networks. Moreover, our collected data may lead to more accurate structural and growth models, which are useful for network analysis and planning. Such models can be used in the design of search algorithms (e.g., by pre-identifying users that are likely to be hubs), in data mining (e.g., by identifying candidate users to monitor), and in system evaluation (e.g., by allowing networks to be simulated over a wide range of sizes).PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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