当相关性误导时：基于混杂因子感知的多视图城市区域表示学习
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
- Aalborg University（奥尔堡大学）
- Chongqing University of Posts and Telecommunications（重庆邮电大学）
- East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对多视图城市区域表示学习中共享潜在因子导致的相关性误导问题，提出混杂因子感知框架CURE，通过分离共享成分并融合残差视图表示，在三个真实城市数据上提升预测性能与鲁棒性。
中文摘要 AI 辅助
城市区域表示学习通常结合异构数据源，如移动流、兴趣点和土地利用信息，以支持移动性分析、公共安全预测和服务需求估计等任务。现有的多视图方法通常通过加强视图间的交互来改善区域嵌入。然而，这类方法往往忽视视图特定的区域结构，并可能传播由共享潜在因子引起的相关性，这会降低下游预测的稳定性。为克服这一主要局限，我们提出CURE，一个用于多视图城市区域表示学习的混杂因子感知框架。CURE首先利用其区域图结构编码每个视图，估计一个共享潜在成分，然后在跨视图交互前减少其投影影响。一个层次化的图感知融合模块随后利用局部和全局区域上下文聚合残差视图表示。在三个真实世界城市的实验表明，CURE提升了预测性能，在缺失和噪声输入视图下保持稳健，并通过共享成分分离和上下文相关的视图加权提供可靠的跨视图集成。
英文摘要
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including mobility analysis, public safety forecasting, and service demand estimation. Existing multi-view methods typically improve region embeddings by strengthening interactions across views. However, such methods often overlook view-specific regional structures and may propagate correlations induced by shared latent factors, which can reduce the stability of downstream predictions. To overcome this major limitation, we propose CURE, a confounder-aware framework for multi-view urban region representation learning. CURE first encodes each view with its regional graph structure, estimates a shared latent component, and then reduces its projected influence before cross-view interaction. A hierarchical graph-aware fusion module subsequently aggregates the residual view representations using local and global regional contexts Experiments on three real-world cities show that CURE improves predictive performance, remains robust under missing and noisy input views, and provides reliable cross-view integration through shared component separation and context-dependent view weighting.
