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140 the community. Thus, we utilise the stopping condition proposed by Bagrow [13] for the Clauset algorithm, based on p-strong communities.2 We evaluate the algorithms of Clauset and Bagrow with values of p = {0.75, 0.8, 0.85, .., 1.0}, as suggested, and select the one with the lowest number of inter-community edges (representing the “best” community). Third, the algorithm of Lou et al. performs iterative adds and deletions, and could therefore remove the original seed nodes from the resulting com- munity. In the case of a single seed node, the authors view the removal of the seed node from the returned community as a failure of the algorithm to detect a com- munity. In order to handle this case for our extended version that accepts a set of seed nodes, we imposed the constraint that we only consider the algorithm of Luo to have found a community if 50% or more of the original seed nodes were present in the resulting community. If not, we do not consider the algorithm to have found a community. Detecting undergraduate communities We now present the results for inferring different attributes for the undergraduate students. For these results, we average over all possible values of each attribute (such as all colleges) into the recall and precision data presented in Figure 7.3. Thus, we feed each algorithm x% of every college and calculate the recall and precision of the 2A community is p-strong when a fraction p of nodes within the community satisfy the criteria that they have more neighbors inside the community than outsidePDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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