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Jingyi Huo

2 accepted papers

2025

Designing Specialized Two-Dimensional Graph Spectral Filters for Spatial-Temporal Graph Modeling

AAAI 2025technical

Spatial-temporal graph modeling is challenging due to the diverse node interactions across spatial and temporal dimensions. Recent studies typically adopt Graph Neural Networks (GNNs) to perform node-level aggregation at different time steps, acting as a series of low-pass graph spectral filters, fo…

2025

Leveraging Heterophily in Spatial-Temporal Graphs for Multivariate Time-Series Forecasting

ICASSP 2025accepted

Multivariate Time-Series (MTS) forecasting is challenging due to the complex spatial-temporal dependencies inherent in MTS data. Recent studies typically adopt spatial-temporal graph models to leverage this information. However, most of these approaches assume homophily in graphs and perform only im…

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