发表机构
Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CANDOR方法，先分解时空动态再学习依赖，在交通和水质预测中优于基线，验证了分解优先原则的有效性。
AI 中文摘要
网络化时空系统中的关系通常是从观测中学习的，而这些观测纠缠了由不同机制控制的动态，掩盖了局部演化内容及其在节点间的传播方式。我们引入了具有有序关系的组件感知网络动态（CANDOR），该方法在学习依赖关系之前先分解局部动态。CANDOR通过持续背景、渐进积累与释放以及稀疏冲击来表示每条轨迹。基于这些组件，一个延迟感知的物理分支在定向拓扑上建模边和样本相关的传播，而一个拓扑无约束的功能分支则从背景动态中发现潜在依赖。上下文自适应融合结合了功能预测、前向传播预测和反向支持预测，训练目标鼓励专门化和语义一致的表示。在两个交通基准和三个长时程水质数据集上的实验跨越了两种不同的时空系统：人为驱动的城市交通和自然演化的河流水质。CANDOR持续优于最强基线，交通预测的MAE最多降低4.31%，水质预测的MSE最多降低5.47%。这些结果确立了先分解后学习依赖作为时空表示学习有效原则的地位。
英文摘要
Relations in networked spatiotemporal systems are often learned from observations that entangle dynamics governed by different mechanisms, obscuring what evolves locally and how it propagates across nodes. We introduce Component-Aware Network Dynamics with Ordered Relations (CANDOR), which decomposes local dynamics before learning their dependencies. CANDOR represents each trajectory through a persistent background, gradual accumulation and release, and sparse shocks. Conditioned on these components, a delay-aware physical branch models edge and sample-dependent propagation over directed topology, while a topology-unconstrained functional branch discovers latent dependencies from background dynamics. Context-adaptive fusion combines functional, forward-propagation, and reverse-support forecasts, with training objectives encouraging specialized and semantically consistent representations. Experiments on two traffic benchmarks and three long-horizon water-quality datasets span two distinct spatiotemporal systems: human-driven urban traffic and naturally evolving river water quality. CANDOR consistently outperforms the strongest baselines, reducing MAE by up to 4.31% for traffic and MSE by up to 5.47% for water-quality forecasting. These results establish decomposition before dependency learning as an effective principle for spatiotemporal representation learning.
