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-解码器框架来预...
1. 本文亮点 使用DTW算法计算时间序列相似度,生产不同的子图,然后将多个子图和预先给定的空间领接矩阵集成成为一个时空融合图,得到隐藏的时空依赖关系。 引入门控膨胀卷积算法,并提出一种新的空间图和时间图的融合方法。 2. 现有方法的局限性 大多数现有模型仅利用给定的空间邻接矩阵进行图形建模,在对邻接矩阵建模时...
First, graph neural networks(GNNs) [20] are one of the most promising deep neural networks in machine learning for learning and capturing the underlying relationships and properties in non-Euclidean domains. Graph convolution network(GCN) [21] successfully learns node representation using a localized...
This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the...
Graph-based deep learning Graph neural networks Survey 1. Introduction Spatial transcriptomics technologies have facilitated the profiling of genome-wide readouts and the documentation of the spatial locations of individual cells [1]. This wealth of information on gene expressions and their spatial contex...
Graph Convolution Neural Network - Spatial Convolution 图卷积神经网络 — 空域卷积详解,程序员大本营,技术文章内容聚合第一站。
最近,图神经网络(Graph Neural networks, GCNs)将卷积神经网络(convolutional Neural networks, CNNs)推广到任意结构的图形,受到越来越多的关注,并成功地应用于许多应用中,如图像分类(Bruna et al. 2014)、文档分类(Defferrard, Bresson, and Vandergheynst 2016)和半监督学习(Kipf和Welling 2017)。然而,前面沿着这...
Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting总结,程序员大本营,技术文章内容聚合第一站。
2) The susceptibility of the existing graph neural networks (GNNs) to oversmoothing reflects an inherent limitation of these networks. 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...
from paper:Benchmarking Graph Neural Networks Anisotropic GNNs 由于在图(graph)中没有特定的方向,比如在image中的上下左右,那么在比如上面Sukhbaatar的图卷积网络中的消息传递是各向同性的,简单来说就是把邻居看得同等重要。然而,不同的邻居之间的重要性应该是不一样的! 因此需要在聚合过程中考虑各向异性,比如根据...