TopoST：面向城市时空基础模型的拓扑条件节点专用化方法
TopoST: Topology-Conditioned Node Specialization for Urban Spatio-Temporal Foundation Models
AI 导读
研究者提出城市时空基础模型 TopoST，在 20+ 城市数据集分析基础上，将局部子图共享学习与拓扑条件节点专用化相结合。其核心是通过共享的拓扑到参数映射生成节点专属时空依赖参数，无需目标域嵌入或模型更新即可对未见节点直接专用化。实验显示 TopoST 在未见城市和任务上具备显著的零样本泛化能力，代码已开源。
正文 · 原文
Abstract: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 this https URL.
| Subjects: | Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15440 [cs.SI] |
| (or arXiv:2611.15440v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15440 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kaiyun Wei [view email]
[v1]
Wed, 11 Nov 2026 13:14:06 UTC (280 KB)
来源：arXiv cs.AI · arxiv.org