其中,Gconv(⋅)是一个图卷积层。图卷积递归网络(Graph Convolutional Recurrent Network, GCRN)[71] 将LSTM网络与ChebNet [21] 结合在一起。扩散卷积递归神经网络(Diffusion Convolutional Recurrent Neural Network, DCRNN)[72] 将提出的扩散图卷积层(方程18)结合到GRU网络中。此外,DCRNN采用了编码器-解码器框架来...
Spatial Graph Convolutional Neural Network: weight function 在图片中,卷积核和图片的piex是一一对应的,因为确定中心x之后,其他的就直接对应上了,因为图片具备一定的空间顺序,就像上图讲的。但是对于我们骨骼图来说,没有一个特定的空间结构,那么一个可行的办法就是对图中的节点进行标记。 这里提到了将邻居结点进行...
论文:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting 或者是:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting GitHub:https://github.com/zezhishao/STEP KDD 2022的论文。 摘要 多变量时间序列(MTS)预测在广...
论文:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting 或者是:Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting GitHub:https://github.com/zezhishao/STEP KDD 2022的论文。 摘要 多变量时间序列(MTS)预测在广...
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting ICML2022 论文地址:https://proceedings.mlr.press/v162/lan22a.html 代码地址:https://github.com/SYLan2019/DSTAGNN 作者:Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li 一个用于时空...
As the network layers deepen, all node representations tend to converge to a uniform value, which greatly affects the ability of the employed model to capture long-term dependencies, especially long-term temporal dependencies. To this end, the spatial-temporal graph neural ODE network (STG-NODE)...
Dynamic Spatial Graph Convolution Network 我们利用学习到的动态空间结构对基于扩散GCN的过程进行改进,从而捕获动态空间关系。这个新模块也被称为动态空间GCN (DSGCN)。 我们最终利用FFN来增强Dynamic GCN的表达能力 实验 两个公共开源的交通数据集,其统计数据如表: ...
graph convolutional neural networksGeodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of ...
Taking advantage of spatial transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes ...
By coupling proposed SAP module with popular spatial-temporal graph neural networks, e.g. MSG3D, it achieves new state-of-the-art accuracy on ... R Hou,Z Wang 被引量: 0发表: 2021年 Feature Selection Based Reverse Design of Doubly Reinforced Concrete Beams Chained Training Scheme (CTS) and...