TopoST 提出拓扑条件节点特化机制，实现城市时空基础模型零样本泛化
TopoST: Topology-Conditioned Node Specialization for Urban Spatio-Temporal Foundation Models
做城市时空预测的可以看看这篇。它不从节点 ID 学嵌入，而是用局部图拓扑直接生成节点参数，新城市不用微调就能跑，20+ 数据集验证过零样本效果，代码也开源了。
论文 TopoST 针对城市时空基础模型的节点异质性问题提出新方案。作者分析了 20+ 个覆盖多种城市场景的数据集，发现不同数据集间存在重复出现的局部图结构，同时局部子图内部节点动态差异显著。
TopoST 的核心做法是共享学习与节点特化结合：通过一个可迁移的拓扑到参数映射（topology-to-parameter mapping），用与节点身份无关的拓扑表示生成节点专属的时间和空间依赖参数。这使得模型无需目标城市的专属嵌入或参数更新，就能直接对未见节点完成特化。
实验结果显示 TopoST 在未见城市和未见任务上取得零样本泛化成绩，代码已在 GitHub 开源。该思路为跨城市部署时空预测模型提供了一条减少迁移成本的路径，对智慧交通、城市计算方向的研究者和工程团队有直接参考价值。
TopoST: Topology-Conditioned Node Specialization for Urban Spatio-Temporal Foundation Models
Urban spatio-temporal foundation models must share knowledge across diverse urban scenarios while accommodating heterogeneous node dynamics. Our analysis of 20+ datasets spanning diverse urban scenarios reveals recurring local graph structures across datasets alongside substantial node heterogeneity within local subgraphs. Motivated by these observations, we propose TopoST, which combines shared learning over local subgraphs with topology-conditioned node specialization. The key insight is to make node specialization itself transferable through a shared topology-to-parameter mapping. This mapping uses identity-independent topological representations to generate node-specific temporal and spatial dependency parameters, enabling direct specialization for unseen nodes without target-specific embeddings or model updates. Extensive experiments demonstrate that TopoST achieves remarkable zero-shot generalization across unseen cities and tasks, highlighting topology-conditioned node specialization as an effective mechanism for transferable urban spatio-temporal foundation models. Code is available at https://github.com/weikaiyun/TopoST.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include TopoST, 时空预测, 零样本泛化, 城市计算.
Source attribution: curated from arXiv cs.AI (@Kaiyun Wei, Yue Xu, Wenying Duan, Zichen Xu, Shuai Ma, Xiaoxi He).