Sensitivity of the resilience of congested random networks to rolloff and offset in truncated power-law degree distributions |
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| Institution: | 1. Department of Economics and Law, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany;2. Resource Economics Group, Humboldt-Universität zu Berlin, Germany;1. School of Graduate Psychology, Pacific University, Hillsboro, OR, USA;2. Mindful Badge Initiative, Hillsboro, OR, USA;3. Stress Reduction Clinic, Hillsboro, OR, USA;4. College of Pharmacy and School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN, USA;5. Departments of Psychology, Psychiatry, Neurology and Neurosurgery, Douglas Institute, McGill University, Montreal, Quebec, Canada |
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| Abstract: | Random networks were generated with the random configuration model with prescribed truncated power-law degree distributions, parameterized by an exponent, an offset, and an exponential rolloff. As a model of an attack, each network had exactly one of its highest degree nodes removed, with the result that in some cases, one or more remaining nodes became congested with the reassignment of the load. The congested nodes were then removed, and the “cascade failure” process continued until all nodes were uncongested. The ratio of the number of nodes of the largest remaining cluster to the number of nodes in the original network was taken to be a measure of the network's resiliency to highest-degree node removal. We found that the resiliency is sensitive to both rolloff and offset (but not to cutoff) in the degree distribution, and that rolloff tends to decrease resiliency while offset tends to increase it. |
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