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215 networks, we conducted a large-scale measurement study that collected data on the social networks of four popular sites, covering over 11 million users and 328 million links. Surprisingly, the results showed that the social network graphs contained in different sites shared a number of graph-theoretic properties, even though the sites have very different goals, mechanisms, and policies. These results have a number of implications for system designers. For example, all the networks contain a dense core of popular users that holds the network together; any information flowing through the network must traverse this core, implying that these users will naturally have significant influence on the spread of information. This was the first study to collect data at large scale and the first study to collect data on multiple social networks. Moreover, we were the first to make the collected data available to the research community; the data is currently in use by more than 100 research groups. The static graph structure present in online social networks reflects the process by which users create links; to understand this process, it is necessary to observe how the networks change and grow over time. Thus, we conducted a second measure- ment study that collected data from multiple online social networks by crawling the network daily for more than three months. This was the first study to collect net- work growth data at significant scale and at fine temporal resolution. The analysis of this growth data provides intuitive explanations for a number of the observed struc- tural properties. The results of our study can be used as the basis for constructing synthetic networks that reflect both global and local characteristics of online socialPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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