术语(Terminology) Aggregation Aggregation是Convolution在GNN中的推广。Aggregation就是在某一个layer中用某node及其neighbor的feature得到下一个layer中该node的feature。 Readout Readout有点
Spatial-based convolution将卷积神经网络的思想借鉴到图神经网络中(卷积神经网络的卷积核是将卷积核滑动所对应范围内的像素进行聚合),即基于节点的邻居对图进行聚合。 Spectral-based convolution是基于信号领域的方法来进行。 上述两类方法的划分为:GAT和GCN是最常用的模型 二、Spatial-based convolution 回顾一下卷积神...
Aggregation Aggregation是Convolution在GNN中的推广。Aggregation就是在某一个layer中用某node及其neighbor的feature得到下一个layer中该node的feature。 Readout Readout有点像是全连接在GNN中的推广。Readout就是汇总整个图的信息,最终得到一个特征来表示这整个图(Graph Representation)。 NN4G(Neural Network for Graph)...
Aggregation Aggregation是Convolution在GNN中的推广。Aggregation就是在某一个layer中用某node及其neighbor的feature得到下一个layer中该node的feature。 Readout Readout有点像是全连接在GNN中的推广。Readout就是汇总整个图的信息,最终得到一个特征来表示这整个图(Graph Representation)。 NN4G(Neural Network for Graph)...
2.3 Spatial-temporal attention-based convolutional network We combine the CNN, STAN and fusion network to construct a spatial-temporal attention-based convolution network (STACN); then, we use the fusion network to generate the final output, as shown in Fig. 1. The workflow is as follows. Fir...
In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the...
本文具体介绍Google DeepMind在15年提出的Spatial Transformer Networks,相当于在传统的一层Convolution中间,装了一个“插件”,可以使得传统的卷积带有了[裁剪]、[平移]、[缩放]、[旋转]等特性。 理论上,作者希望可以减少CNN的训练数据量,以及减少做data argument,让CNN自己学会数据的形状变换。相信这篇论文会启发很多新...
Attention Based Spatial-Temporal Graph Convolutional Networks 2.1 Spatial-Temporal Attention 2.2 Spatial-Temporal Convolution 2.3 Multi-Co... 查看原文 论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》 预测交通流,该问题最大的挑战是交通流数据的高度非线性...
1). We proposeST-GCN, a generic graph-based formulation for modeling dynamic skeletons, which is the first that applies graph-based neural networks for this task. 2). We propose several principles in designing convolution kernels inST-GCN to meet the specific demands in skeleton modeling. ...
GLU-STGCN: A graph convolution network with a gating mechanism GeoMAN: A multi-level attention-based recurrent neural network model 实验结果 从表1可以看出,就所有评估指标而言,ASTGCN在两个数据集上都取得了最佳性能。传统时间序列分析方法的预测结果通常并不理想,这表明这些方法对非线性和复杂交通数据建模的...