AI 中文总结
该研究提出模糊网络跳跃模型，结合空间与时间正则化的交替优化方案，在模拟及旧金山交通网络数据上验证其聚类性能优于对比方法，可有效处理图结构数据的软动态聚类问题。
AI 中文摘要
我们提出一种模糊网络跳跃模型，用于对加权图节点索引的时变观测值进行聚类。该框架支持灵活的图表示，空间和时间正则化项可促进相连节点及连续时间点间的平滑软聚类分配。估计通过高效交替优化方案完成，该方案利用正则化项的二次结构。涵盖不同空间依赖程度和聚类重叠水平的模拟研究显示，所提方法能准确恢复真实隶属概率，且优于现有聚类方法。将其应用于旧金山市交通网络数据，可识别出可解释的交通状态，并揭示其随时间及相连路段的演变情况。
英文摘要
We introduce a fuzzy network jump model for clustering time-varying observations indexed by the nodes of a weighted graph. The framework allows flexible graph representations with spatial and temporal regularization promoting smooth soft cluster assignments across connected nodes and consecutive time points. Estimation is performed through an efficient alternating optimization scheme that exploits the quadratic structure of the regularization terms. A simulation study covering different levels of spatial dependence and cluster overlap shows that the proposed method accurately recovers the true membership probabilities and outperforms competing clustering methods. An application to traffic-network data for the city of San Francisco identifies interpretable traffic regimes and reveals their evolution over time and across connected road segments.
