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Jingtian Ma

2 accepted papers

2026

Hierarchical Frequency-Decomposition Graph Neural Networks for Road Network Representation Learning

AAAI 2026technical

Road networks are critical infrastructures underpinning intelligent transportation systems and their related applications. Effective representation learning of road networks remains challenging due to the complex interplay between spatial structures and frequency characteristics in traffic patterns.

Cited by 0SourcePDFScholar
2026

Seeking Commonality, Preserving Specificity: A Spectral-Aware Hierarchical Framework for Cross-City Road Representation Learning

ICML 2026poster

Learning unified road representations across diverse cities is a pivotal challenge in urban computing. However, existing approaches predominantly focus on single-city modeling, failing to handle the distribution shifts caused by heterogeneous urban layouts. We identify *spectral misalignment*, manif…

Cited by 0SourceScholar