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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems

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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems ( online-social-networks-measurement-analysis-and-applications )

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1 0.8 0.6 0.4 0.2 0 184 Random Proximity YouTube 1 10 100 False classification probability (%) Figure 8.10 : Proportion of messages delivered versus false classification probability for wanted messages. be sent. However, it seems very unlikely that the moderator of a sharing site would misclassify content at such a high rate. Resilience to link attacks In a potential security attack discussed in Section 8.2, malicious users attempt to exhaust credit on a set of links inside the trust network, i.e., links other than the attackers’ adjacent links. If successful, this attack could disrupt communication for innocent users. To evaluate whether a real-world social network is susceptible to this attack, we performed a min-cut analysis of the YouTube social network. Assuming uniform link weights of one, we calculated the min-cuts1 between 3,000 randomly selected pairs of users. We then looked for cases in which the set of links 1A min-cut is a minimal set of links that, if removed, partitions two users; note that several such cuts can occur between two users. Proportion messages delivered

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