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xviii 7.1 Normalized mutual information versus the fraction of users who reveal their community for Rice undergraduates. Revealing more information naturally leads to partitionings with higher correlations, especially for the college and year attributes. This result shows that different attributes can be accurately inferred with as few as 20% of usersrevealingtheirattributes. ..................... 132 7.2 Normalized mutual information versus the fraction of users who reveal their community for Rice graduate students. . . . . . . . . . . 133 7.3 Recall and precision of single community detection for Rice undergraduates 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 inferringmatriculationyear. ....................... 140 7.4 Recall and precision for matriculation year community detection for Rice undergraduates for our algorithm. Individual lines are shown for each matriculation year. Certain values of user attributes are easier todetectthanothers............................ 141 7.5 Detail on recall and precision for college inference for Rice undergraduateswithouralgorithm.................... 142PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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