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Author

Min Zhang

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Book Open access Aug 2026

HURST: Heterogeneity-Adaptive Urban Foundation Models for Spatiotemporal Prediction via Self-Partitional Mixture-of-Spatial-Experts

HURST is a Heterogeneity-Adaptive URban Foundation Model for Spatio-Temporal Prediction that is capable of capturing the spatial pattern of heterogeneity underlying the urban setting to enhance the UFM's performance and presents two key technical innovations.

Zirui Zhou, Xun Zhou, Kanyu Bao et al. · 0 citations
Conference May 2026

Listing Minimal Cores in Large Real-World Graphs

Cohesive subgraph mining is a fundamental task in graph data analytics. We re-visit the problem of listing all minimal $k$-cores, where a $k$-core is a subgraph in which every vertex has degree at least $k$, and minimality requires that no proper subset remains a $k$-core. Existing methods are computationally prohibiti...

Yukai Sun, Kaiqiang Yu, Shengxin Liu et al. · 0 citations

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