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0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 183 Random Proximity YouTube 1 10 100 Maximum credit imbalance per link (L) Figure 8.9 : Amount of unwanted communication received by good users as the maximum credit imbalance per link is varied. good users increases, as expected. Finally, we examine the sensitivity of Ostra to the false positive rate of legitimate users’ message classification. In other words, if users incorrectly mark other good users’ messages as unwanted, how often are users blocked from sending message? We show how this probability of false classification affects the proportion of messages that cannot be sent in Figure 8.10. As can be seen, even a high false positive rate of 30% results in only a few blocked messages. This resiliency results from the rich connectivity of the social network (i.e., if one link becomes blocked, the users can route through other friends), and the fact that the false positive rate affects all users equally. In the case of the content-sharing site, because all paths intersect, good users are blocked more quickly as the amount of content that is marked as unwanted increases. For example, when the false classification rate is 64%, about 40% of messages cannot Unwanted messages received (messages/user/week)PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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