Yang Yang, Nishikawa Takashi, Motter Adilson E
Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208, USA.
Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208, USA.
Phys Rev Lett. 2017 Jan 27;118(4):048301. doi: 10.1103/PhysRevLett.118.048301.
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.
在一个网络中，局部干扰可能会传播，并最终导致系统的很大一部分在级联事件中失效，这些级联事件易于概念化，但极难预测。在此，我们开发了一个统计框架，该框架仅通过纳入两个要素就能预测级联规模分布：单个组件的脆弱性以及组件组的共同易损性（即它们一起失效的倾向）。以电网中的级联为例，我们表明组件故障之间的相关性定义了结构化的、且往往大得出奇的共同易损组件组。除了对停电研究的意义之外，这些结果还为理解金融系统、食物网以及一般复杂网络中的级联提供了见解和一个新的建模框架。
