ST-Meta Graph Reconstruction进一步设计用于通过重建不同城市的结构关系来进行结构感知元训练。 ST-GFSL 的端到端学习过程遵循基于MAML的episode learning。通过模拟目标城市的小样本场景,对批量的小样本训练任务进行采样,得到适应性强的基础模型。 Spatio-Temporal Neural Network 时空神经网络(STNN)可以分为特征提取器和...
To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-...
20. Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction 标题:时空图神经点过程用于交通拥堵事件预测 作者:Guangyin Jin, Lingbo Liu, Fuxian Li, Jincai Huang 内容:该工作针对交通拥堵事件预测问题,提出了空间-时间图神经点过程框架STGNPP,先通过空间-时间图学习模块从历史交通状态和...
more advanced neighborhood aggregation methods54, scalable inference55and domain-specific applications20have been introduced. In general, a graph neural network performsRrounds of message passing, after which all nodes’ latent features
DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting A novel DEST-GNN that captures spatio-temporal correlations for multi-site PV power forecasting.Leveraged sparse spatio-temporal attention and adaptive GCN... Y Yang,Y Liu,Y Zhang,.....
STAM: A Spatiotemporal Aggregation Method for Graph NeuralNetwork-based Recommendation STAM:一种基于图神经网络的推荐的时空聚合方法 来源:WWW 2022 摘要:现有的基于图神经网络的推荐方法通常关注于如何从空间结构信息的角度来聚合信息,但关于邻居的时间信息却没有得到充分的探索。在这项工作中,作者提出了一种时空聚...
STGCN in traffic: https://github.com/FelixOpolka/STGCN-PyTorch. Seq2Seq in Neural machine translation: https://www.tensorflow.org/tutorials/text/nmt_with_attention.About Spatio-temporal graph neural network for predicting COVID-19 new cases in Korea. Topics timeseries forecasting graph-neural...
Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network data-driven method for predicting the remaining useful life of bearings based on a deep graph convolutional neural network with spatiotemporal domain ... P Li,X Liu,Y Yang - 《Sensors》 被引...
A Graph Neural Network with Spatio-Temporal Attention for Multi-Sources Time Series Data: An Application to Frost Forecast The model considers spatial and temporal relations while processing multiple time series simultaneously. Performing predictions of 6, 12, 24, and 48 h in ... N Sanchez-Pi -...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the