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7.2.3 Summary In both the Rice undergraduate and graduate student populations, we observe that users with similar attributes tend to be friends in the social network. Moreover, we observe a significant community structure, indicated by a high modularity value, for the communities defined by users who share certain attributes. We also observe that multiple overlapping community structures exist. For the undergraduates, we observe significant modularity when partitioning according to residential college and matriculation year. For the graduate students, we observe significant modularity when partitioning according to department and a weaker modularity according to matriculation year. 7.3 Detecting communities In the previous section, we observed that the undergraduate network, and to a lesser extent the graduate network, contained communities that were correlated with mul- tiple attributes. For the undergraduates, the partitionings according to college and matriculation year both showed significant correlation with the communities in the network. For the graduates, partitionings according to department and year showed similar behavior. We now consider the use of automatic clustering algorithms to detect a specific community among the multiple communities that exist. To do so, we split the problem into two parts: first, if partial membership in- formation about all communities in the network is known, we examine the problem 131PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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