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141 result. We repeat this experiment five times for each college and fraction revealed, and then average over all colleges to obtain the data in Figure 7.3 (a). 1.00 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 Norm. Cond. Luo Bagrow Clauset 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (a) College 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (b) Major 0 0.2 0.4 0.6 0.8 1 Fraction of Users Revealed (c) Year Figure 7.3 : Recall and precision of single community detection for Rice undergradu- ates for multiple algorithms. Good performance is observed for our algorithm (Norm. Cond.) for college and year; detecting users with the same major performs poorly due to the low correlation with communities in the network. The algorithm of Luo performs well at inferring college but does not perform well for inferring matriculation year. As a detailed example, Figure 7.4 presents the recall and precision for each of the matriculation years as different number of users are revealed. A number of interesting observations can be made about the results. First, the performance varies across the different matriculation years; the freshmen and sophomores appear to be the easiest to detect, followed by the juniors and seniors. Second, detecting all of the matriculation years shows good performance once 20% to 30% of the users is revealed. Third, note that the precision naturally deteriorates once very high fractions of the users in each Recall PrecisionPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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