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124 from the Facebook [49] social network. For each student, we gather attributes like major(s) of study, year of matriculation, and dormitory, to see if communities in the network align with these attributes. We find that users tend to form links to other users who share the same attributes, and that users who share certain attributes define strong communities in the social network. Second, we examine how well existing techniques can detect communities. We find that existing approaches often perform poorly on our data set, sometimes returning a large part of the network (or the whole network) as a community. We demonstrate that this poor performance is due to the use of community-rating metrics that are biased towards large communities. Third, we propose and evaluate a new algorithm that can accurately infer mem- berships of multiple, potentially overlapping communities, when given information about a small subset of the community members. In practice, this means that if even as few as 20% of users provide community information to social networking sites, the remaining members of the community can be determined from the social network alone with high accuracy. In the following sections, we describe the data we use for our community analysis. We then examine the data set, looking at the correlation between attributes and the links and communities in the network. Finally, we evaluate previously proposed approaches and propose and evaluate new approach for detecting communities in the networkPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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