从数据中揭示动态、社区和关键性
Revealing dynamics, communities and criticality from data
摘要：
生态群落和神经元网络等复杂系统是我们日常生活的重要组成部分。这些系统由通过复杂网络相互作用的单元组成。从数据中预测这些网络动态的突然变化（称为关键转变）的能力对于避免重大中断的灾难性后果非常重要。预测这种变化是一个重大挑战，因为它需要预测参数范围的行为，而系统上没有数据。我们解决这个问题的网络与弱的个人相互作用和混乱的本地动态。我们通过构建一个模型网络来做到这一点，该网络称为{ \em有效网络}，由底层局部动态及其相互作用的统计描述组成。我们表明，这种网络的行为可以分解的紧急确定性组件和{ \em波动}项。传统上，这种波动会被过滤掉。然而，正如我们所展示的，它们是访问交互结构的关键。
英文摘要：
Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behaviour for parameter ranges for which no data on the system is available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an {\em effective network}, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behaviour of such networks can be decomposed in terms of an emergent deterministic component and a {\em fluctuation} term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
