The Impact of Imperfect Information on Network Attack

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Student Presentation

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Charles Hanna and Michelle Girvan


Network models are used in many contexts, including the study of disease spread, power failure cascades, and gene regulation. The important property of system-wide connectedness in networks can be given in terms of the largest interconnected subnetwork, or 'Giant Component'(GC). Attacks on networks to decrease the size of GC are often studied. For example, diseases spread more slowly in networks with a very small GC. In this project, we use a computational model to explore the effectiveness of network attack when the attacker has imperfect information about the network. For Erdös-Rényi networks, we observe that dynamical importance and betweenness centrality-based attacks are surprisingly robust to the presence of a moderate amount of imperfect information and are more effective compared with simpler degree-based attacks even at moderate levels of network information error. In contrast, for scale-free networks the effectiveness of attack is much less degraded by a moderate level of information error. Furthermore, in the Erdös-Rényi case the effectiveness of network attack is much more degraded by missing links as compared with the same number of false links.

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