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1.00 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 145 Norm. Cond. Luo Bagrow Clauset 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (a) Department 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (b) School 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (c) Year Figure 7.6 : Recall and precision for single community detection for Rice graduate students. Good performance is observed for department and school; much weaker performance is seen for year. tiple partitionings according to different attributes. In fact, we found that with as few as 20% of users with known attributes, the remaining users can be classified with over 80% accuracy. Moreover, we proposed a new algorithm that can detect a community when given as input only a few users in the community. We found that this algorithm is able to detect communities in both the undergraduate and graduate student networks when given as few as 10% of the community. Thus, we found that with partial information about users, we are able to detect the multiple community structures that exist with high accuracy. Our work has a number of implications and uses. For example, many of the popular online social networks could directly apply our algorithm in order to detect Recall PrecisionPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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