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144 Majors: Finally, we observe that none of the algorithms are able to infer major; all have extremely low precision. This result is expected, though, since we observed in the previous section that majors do not form significant communities in the network. Detecting graduate student communities We now turn to evaluate our approach on the graduate student network. Figure 7.6 shows how the recall and precision vary as different fractions of the department, school, and matriculation year of graduate students are provided. Inferring the de- partment and school of students shows good performance for all algorithms except for Bagrow’s (as we observed with the undergraduates, the algorithm of Bagrow tended to return a large portion of the network as a community). We find that knowing 20% of the user attributes is sufficient to infer most of the remaining users with high accuracy. However, inferring matriculation year does not perform as well for any algorithm, having low recall and precision. Again, the poor performance at detecting matriculation years can be explained by the data in Section 7.2, which shows that the matriculation years form weak communities in the social network. 7.4 Summary We began this section by asking whether the multiple overlapping community struc- tures that exist in online social networks can be detected. We demonstrated that existing techniques can be “seeded” with attributes provided by users to detect mul-PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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