When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning

2026-09-14

AI Summary

针对多视图城市区域表示学习中因共享潜在因素导致的误导性关联问题，提出CURE框架，通过估计并减少共享潜成分的影响来增强预测稳定性。

Abstract

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.

Problem

• multi-view
• urban region representation learning
• confounder-aware
• shared latent factors
• downstream predictions

Innovation

• confounder-aware
• multi-view learning
• hierarchical graph-aware fusion
• shared latent component reduction
• context-dependent view weighting