每一个 ST-Conv Block 是由两个 Gated Temporal Convolution layer 夹着一个 Graph Convolution layer 组成。之所以,TGC 的 channel number 是 64,SGC 的是 16,是因为原作者认为这种「三明治」结构既可以achieve fast spatial-state propagation from graph convolution through temporal convolutions,又可以helps the ...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 巴拉巴拉 积极向上的懒人1.创新点 第一篇将图卷积用于提取空间和时间信息的文章,没有使用正则卷积和递归单元,使用了完整的卷积结构,在更少的参数下,可以得到更快的训练速度 2.问题描述 通过前[t-m+1,t] 的交通流量...
Graph Convolution层采用Residual Connection与ChebConv或GCNConv结合。原作者对GCNConv的解释存在误读，实际应用中应采用正确的公式。ChebConv利用第一类切比雪夫多项式近似卷积核，通过递推公式降低时间复杂度。GCNConv在特定条件下的简化形式，包含特征值归一化的技巧以防止梯度消失或爆炸。Temporal Gated Convolut...
问题背景:交通流量预测忽略时空依赖性。 提出模型:Spatio-Temporal Graph Convolutional Networks (STGCN)。instead of 常规卷积和递归单元,本文在图上公式化问题,并使用完整的卷积结构构建模型,使得以更少的参数实现更快的训练速度。 流量预测分为:短期(5-30min),中长期(>30min)。 RNN迭代训练会累积误差,并且难训...
Recent research developed Spatio-Temporal Graph Neural Networks (ST-GNNs) to capture the spatiotemporal correlations and achieved superior performance. However, the graph adjacency matrices that most ST-GNNs use are either pre-defined by heuristic rules or directly learned with trainable parameters. ...
Spatio-TemporalGraphConvolutionalNetwork: ImprovingTrafficPrediction with Navigation Data 【混合时空图卷积网络:交通流量预测】 【要点】:将交通路网视为一个以路段为节点的图。 【补充知识】 图卷积:https://zhuanlan.zhihu.com/p/89503068 Spatio-Temporal Attention Based LSTM Networks for 3D Action Recognition ...
Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures To improve the security and reliability of wind energy production, short-term forecasting has become of utmost importance. This study focuses on multi-step... LD Bentsen,ND Warakagoda,R Stenbro,... - ...
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...
The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convoluti...
The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external ...