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143 between 1% and 16% of the college members are provided. The algorithms of Clauset and Bagrow both perform rather poorly at detecting colleges: they each often return a large part of the network as belonging to the college, resulting in a very low precision score. 1.00 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 Figure 7.5 : Detail on recall and precision for college inference for Rice undergraduates with our algorithm. Years: However, for inferring matriculation years, all algorithms have significant recall, but only our algorithm has good precision. In fact, the other algorithms tend to detect the entire graph as a community, which leads to the low precision. Again, we believe that this poor performance is a function of the metrics that the other algorithms use. Since they essentially try to maximize the ratio of intra-community links to inter-community links, they occasionally end up returning the whole graph. 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Fraction of Users Revealed Recall PrecisionPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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