学习综述论文 A Comprehensive Survey on Graph Neural Networks 许多现实世界应用中的图在图结构和图输入这两方面都是动态的。时空图神经网络(STGNNs)在捕捉图的动态性方面占有重要位置。此类别下的方法旨在建模动态节点输入,同时假设连接节点之间的相互依赖性。例如,一个交通网络由路上放置的速度传感器组成,其中边
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...
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have become an emerging research issue for improving the traffic forecasting problem. Specifically, one of the main types of GNNs is the spatial-temporal GNN (ST-GNN), which has been applied to various time-series...
The pioneering work27 concerning spatial-temporal graph convolutional networks (ST-GCNs), which encapsulate human skeleton data within graph frameworks, is particularly important. In this approach, a GCN is used for skeleton-based action recognition. This impetus has pushed GCN-based methods to the ...
Inspired by the recent work on STGNNs, we aim to devise a spatial–temporal deep learning framework for time-evolving social networks. For spatial convolutions, we adopt the graph embedding approach over spectral methods due to two main reasons. Firstly, we need to incorporate node features as...
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。
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods. STGNNs jointly model the spatial and temporal patterns of MTS through graph neural networks and sequential models, significantly improving the prediction accuracy. But limited by model ...
Ignoring this correlation hampers the comprehensive modeling of ST dependencies in multi-sensor data, thus limiting the effectiveness of existing GNNs in learning effective representations. To tackle the aforementioned challenges, this paper introduces a richly connected spatial–temporal graph neural network...
交通论文阅读:Graph WaveNet for Deep Spatial-Temporal Graph Modeling,程序员大本营,技术文章内容聚合第一站。