论文标题
通过深度学习来检测时间序列的结构扰动
Detecting structural perturbations from time series with deep learning
论文作者
论文摘要
小型干扰可以触发复杂系统中的功能故障。一项艰巨的任务是推断网络系统中干扰的结构性原因，以防止灾难。我们提出了一种从深度学习范式借用的图形神经网络方法，以从功能时间序列中推断结构扰动。我们显示，我们的数据驱动方法在满足贝叶斯推断的准确性的同时优于典型的重建方法。我们在各种网络结构上通过流行病扩展，人口动态和神经动态来验证方法的多功能性和性能：随机网络，无标度网络，25个真实的食物 - 网络系统和秀丽隐杆线虫连接。此外，我们报告说，我们的方法对数据腐败是可靠的。这项工作发现了研究现实世界复杂系统的弹性的实用途径。
Small disturbances can trigger functional breakdowns in complex systems. A challenging task is to infer the structural cause of a disturbance in a networked system, soon enough to prevent a catastrophe. We present a graph neural network approach, borrowed from the deep learning paradigm, to infer structural perturbations from functional time series. We show our data-driven approach outperforms typical reconstruction methods while meeting the accuracy of Bayesian inference. We validate the versatility and performance of our approach with epidemic spreading, population dynamics, and neural dynamics, on various network structures: random networks, scale-free networks, 25 real food-web systems, and the C. Elegans connectome. Moreover, we report that our approach is robust to data corruption. This work uncovers a practical avenue to study the resilience of real-world complex systems.
