标题： 用补偿性扰动控制复杂网络 显示英文标题

标题： Controlling Complex Networks with Compensatory Perturbations

摘要： 复杂网络对扰动的响应在生态系统管理、应急响应和细胞重编程等多个领域具有极端重要性。 网络的一个基本特性是，一个节点的扰动可能会影响其他节点，在这个过程中可能导致整个系统或其大部分部分的行为发生变化，甚至崩溃。 近期在代谢网络和食物网网络方面的研究已经证明了这样一个概念：由外部扰动引起的网络损伤通常可以通过应用补偿性扰动来缓解或逆转。 补偿性扰动被限制为物理上可行且可以在网络上实施的扰动。 然而，系统地识别符合这些约束条件的补偿性扰动仍然是一个开放问题。 在此，我们提出了一种方法来构建补偿性扰动，在这些约束条件下可以控制一般网络的命运。 我们的方法考虑了真实复杂网络的全部非线性行为，并且即使在目标状态不可直接访问的情况下，也可以将系统带入期望的目标状态。 在基因网络中的应用表明，当补偿性扰动仅限于网络中一小部分节点时仍然有效，并且当限制在高阶度节点时效果要好得多。 该方法在概念上简单且计算效率高，使其适用于各种领域中大型复杂网络的救援、控制和重编程。 显示英文摘要

摘要： The response of complex networks to perturbations is of utmost importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. A fundamental property of networks is that the perturbation of one node can affect other nodes, in a process that may cause the entire or substantial part of the system to change behavior and possibly collapse. Recent research in metabolic and food-web networks has demonstrated the concept that network damage caused by external perturbations can often be mitigated or reversed by the application of compensatory perturbations. Compensatory perturbations are constrained to be physically admissible and amenable to implementation on the network. However, the systematic identification of compensatory perturbations that conform to these constraints remains an open problem. Here, we present a method to construct compensatory perturbations that can control the fate of general networks under such constraints. Our approach accounts for the full nonlinear behavior of real complex networks and can bring the system to a desirable target state even when this state is not directly accessible. Applications to genetic networks show that compensatory perturbations are effective even when limited to a small fraction of all nodes in the network and that they are far more effective when limited to the highest-degree nodes. The approach is conceptually simple and computationally efficient, making it suitable for the rescue, control, and reprogramming of large complex networks in various domains.