WebSpatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the … WebApr 14, 2024 · Download Citation DP-MHAN: A Disease Prediction Method Based on Metapath Aggregated Heterogeneous Graph Attention Networks Disease prediction as …
Spatial-Temporal Synchronous Graph Convolutional …
WebJan 29, 2024 · Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic systems with complex spatial and temporal dependencies such as city traffic networks. In this research, we apply three state-of-the-art ST-GNN architectures, i.e. Graph WaveNet, MTGNN and … WebMay 9, 2024 · In this paper, we propose an adaptive graph co-attention networks (AGCAN) to predict the traffic conditions on a given road network over time steps ahead. We introduce an adaptive graph modelling method to capture the cross-region spatial dependencies with the dynamic trend. We design a long- and short-term co-attention network with novel ... grass cutting services in glen burnie md
aptx1231/Traffic-Prediction-Open-Code-Summary - Github
WebGraph WaveNet for Deep Spatial-Temporal Graph Modeling 摘要： 本文提出了一个新的时空图建模方式，并以交通预测问题作为案例进行全文的论述和实验。 交通预测属于时空任务，其面临的挑战就是复杂的空间依赖性 … WebWith the development of deep learning on graphs, powerful methods like graph convolutional net- ... ST-ResNet (Zhang, Zheng, and Qi 2024) is a CNN based deep residual network for citywide crowd flows pre-diction, which shows the power of deep residual CNN on ... Graph WaveNet (Wu et al. 2024) designs a self-adaptive matrix to WebZonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2024. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In Proc. of IJCAI. Google Scholar Cross Ref; Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2024. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proc. of AAAI. 3482--3489. grass cutting services in my area