Universal resilience patterns in complex networks
Universal resilience patterns in complex networks
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DOI：
10.1038/nature16948
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发表时间：
2016-02-18
期刊：
影响因子：
64.8
通讯作者：
Barabasi, Albert-Laszlo
中科院分区：
文献类型：
--
作者：
Gao, Jianxi;Barzel, Baruch;Barabasi, Albert-Laszlo
弹性，系统在发生错误，故障和环境变化时调整其活动以保持其基本功能的能力，是许多复杂系统的定义属性。尽管对人类健康（2），经济（3）和环境（4）造成广泛的后果，但导致失去连续性的事件-从技术系统的级联故障（5）到生态网络的大规模破坏（6）-很少是可预测的，而且往往是不可逆转的。这些局限性根源于理论上的差距：目前的弹性分析框架旨在处理具有少数相互作用组件的低维模型（7），并且不适合由大量组件组成的多维系统，这些组件通过复杂网络相互作用。在这里，我们通过开发一套分析工具来弥合这一理论差距，这些工具可以识别多维复杂系统的自然控制和状态参数，帮助我们获得有效的一维动力学，从而准确预测系统的弹性。建议的分析框架，使我们能够系统地分离系统的动态和拓扑结构的作用，不同的网络崩溃到一个单一的通用弹性功能的行为。分析结果揭示了可以增强或减弱复原力的网络特征，提供了防止生态、生物或经济系统崩溃的方法，并指导了对内部故障和环境变化具有复原力的技术系统的设计。
Resilience, a system's ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems(1). Despite widespread consequences for human health(2), the economy(3) and the environment(4), events leading to loss of resilience-from cascading failures in technological systems(5) to mass extinctions in ecological networks(6)-are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components(7), and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system's resilience. The proposed analytical framework allows us systematically to separate the roles of the system's dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.