AI 中文总结
该研究针对网络系统同步转变的预警问题，基于随机Kuramoto模型，比较三种预警信号与三类哨兵选择策略，发现仅用少量动态选择的哨兵即可实现接近全观测的预警性能。
AI 中文摘要
在许多网络系统中，预测集体同步的发生至关重要，但观测所有振子往往不切实际。我们研究能否通过少量被监测的“哨兵”节点检测同步转变。在网络上使用随机Kuramoto模型，我们数值比较三种预警信号：局域序参量、其时域方差，以及去除个体振子相位平均旋转趋势后的相位方差。我们还比较基于节点动力学、度和随机采样的哨兵选择策略。结果显示，在部分观测下，局域序参量和去趋势相位方差提供的预警信号远强于局域序参量的方差；根据节点在转变附近的动力学行为选择节点，其性能始终优于其他哨兵选择方法。仅用⌊ln N⌋个经动态选择的哨兵，两项成功指标即可接近观测全部N个节点时的性能。这些结果表明，当预警信号和被监测节点选择恰当时，可通过稀疏观测预测同步转变。
英文摘要
Anticipating the onset of collective synchronization is important in many networked systems, yet observing every oscillator is often impractical. We investigate whether synchronization transitions can be detected from a small set of monitored, or sentinel, nodes. Using a stochastic Kuramoto model on networks, we numerically compare three early warning signals: the local order parameter, its temporal variance, and the variance of individual oscillator phases after removing their mean rotational trends. We also compare sentinel-selection strategies based on node dynamics, degree, and random sampling. We show that, under partial observation, the local order parameter and the variance of detrended phases provide substantially stronger warning signals than the variance of the local order parameter. Selecting nodes according to their dynamical behavior near the transition consistently improves performance over other sentinel-selection methods. With only $\lfloor\ln N\rfloor$ dynamically selected sentinels, two success indicators approach the performance obtained by observing all $N$ nodes. These results demonstrate that synchronization transitions can be anticipated from sparse observations when the warning signal and monitored nodes are chosen appropriately.
Comments14 pages, 5 figures, 3 tables
