most citedResilience Inference for Supply Chains with Hypergraph Neural Network

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Resilience Inference for Supply Chains with Hypergraph Neural Network

Zetian Shen, Hongjun Wang, Jiyuan Chen +1

Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate…

cs.LG2024

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting

Hongjun Wang, Jiyuan Chen, Lingyu Zhang +2

Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. How…

cs.LG2024

STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting

Hongjun Wang, Jiyuan Chen, Tong Pan +4

Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture comple…

cs.LG2024

Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario

Hongjun Wang, Jiyuan Chen, Tong Pan +4

Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly ev…

cs.LG2024

Robust Traffic Forecasting against Spatial Shift over Years

Hongjun Wang, Jiyuan Chen, Tong Pan +4

Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both t…