| Literature DB >> 28186802 |
Yang Yang1, Takashi Nishikawa1,2, Adilson E Motter1,2.
Abstract
In a network, a local disturbance can propagate and eventually cause a substantial part of the system to fail in cascade events that are easy to conceptualize but extraordinarily difficult to predict. Here, we develop a statistical framework that can predict cascade size distributions by incorporating two ingredients only: the vulnerability of individual components and the cosusceptibility of groups of components (i.e., their tendency to fail together). Using cascades in power grids as a representative example, we show that correlations between component failures define structured and often surprisingly large groups of cosusceptible components. Aside from their implications for blackout studies, these results provide insights and a new modeling framework for understanding cascades in financial systems, food webs, and complex networks in general.Year: 2017 PMID: 28186802 DOI: 10.1103/PhysRevLett.118.048301
Source DB: PubMed Journal: Phys Rev Lett ISSN: 0031-9007 Impact factor: 9.161