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216 networks, leading to better structural and growth models. We also made the data in this study available to the research community and it is currently in use by more than 25 research groups. We have also examined the community structure of online social networks. We found that individual users are often members of multiple overlapping communities, but that existing algorithms for detecting communities do not perform well on real data from an online social network. We addressed this limitation by devising a new algorithm that can accurately detect multiple overlapping communities when given information about a small subset of the community members. In practice, even if only 10% of users provide community information to social networking sites, the remaining community members can be determined by this algorithm with high accuracy. We demonstrated that this approach can identify communities at a range of scales on a university network: small communities such as sports teams, larger communities such as dormitories, and even very large communities such as every student matriculating in the same year. While valuable, the measurement studies described above are not an end per se, rather, they are a first-order concern when trying to build better systems. For example, links between users in online social networks can represent trust (e.g., users who know each other in the offline world) and shared interest (e.g., users who belong to the same community). We have built two new information systems that exploit each of these properties to solve open problems – these are briefly described below.PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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