PHGNet: 原型引导的超图构建用于异质时空预测
PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
AI总结：
提出基于原型引导超图构建的时空预测框架PHGNet，通过原型学习机制自适应地将模式相似节点分配到超边以捕获高阶交互，并引入全局-局部节点表示模块和迭代残差细化与时间查询注意力机制提升预测精度。
摘要：
作为智能交通系统的核心任务，交通预测在城市交通管理中起着关键作用。准确的交通预测依赖于对复杂时空依赖关系的建模，而由于交通系统中的空间异质性，这本身就具有挑战性。尽管取得了显著进展，大多数现有方法仍局限于成对空间依赖建模，难以捕获具有相似交通模式的节点之间的动态高阶交互。为了解决这个问题，我们提出了PHGNet，一种基于原型引导超图构建的新型时空预测框架。在PHGNet的核心，设计了一种原型学习机制，自适应地将模式相似的节点分配到超边，从而捕获具有时变结构的高阶交互。为了提高动态超图构建的可靠性，我们进一步开发了一个全局-局部节点表示模块来提取时间一致的特征。对于预测，引入了迭代残差细化和时间查询注意力机制，以提高预测精度并支持高效的并行解码。在多个真实世界数据集上的大量实验表明，与最先进的方法相比，PHGNet实现了优越的预测性能。
英文摘要：
As a core task in intelligent transportation systems, traffic forecasting plays a critical role in urban traffic management. Accurate traffic forecasting relies on modeling complex spatiotemporal dependencies, which is inherently challenging due to spatial heterogeneity in traffic systems.Despite significant progress, most existing methods are still limited to pairwise spatial dependency modeling, making it difficult to capture dynamic high-order interactions among nodes with similar traffic patterns. To address this issue, we propose PHGNet, a novel spatiotemporal forecasting framework based on prototype-guided hypergraph construction. At the core of PHGNet, a prototype learning mechanism is designed to adaptively assign pattern-similar nodes to hyperedges, thereby capturing high-order interactions with time-varying structures. To improve the reliability of dynamic hypergraph construction, we further develop a global-local node representation module to extract time-consistent features. For forecasting, iterative residual refinement and Temporal Query Attention are introduced to improve forecasting accuracy while supporting efficient parallel decoding. Extensive experiments on multiple real-world datasets demonstrate that PHGNet achieves superior predictive performance compared with state-of-the-art methods.
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。
