论文地址: https://arxiv.org/pdf/1906.00121.pdfarxiv.org/pdf/1906.00121.pdf 代码地址: GitHub - nnzhan/Graph-WaveNet: graph wavenetgithub.com/nnzhan/Graph-WaveNet 该论文主要用于解决时空建模问题上图结构不确定性问题,通过自适应的可学习的邻
Graph WaveNet for Deep Spatial-Temporal Graph Modeling 摘要:本文提出了一个新的时空图建模方式,并以交通预测问题作为案例进行全文的论述和实验。交通预测属于时空任务,其面临的挑战就是复杂的空间依赖性和…
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。
Graph WaveNet for Deep Spatial-Temporal Graph Modeling 用于深度时空图模型的Graph WaveNet 期刊:IJCAI2019 作者:Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang 论文地址:https://www.ijcai.org/Proceedings/2019/0264 代码地址:https://github.com/nnzhan/Graph-WaveNet 这是一篇很经典的老...
Graph WaveNet for Deep Spatial-Temporal Graph Modeling 一 作者介绍 本文的作者是悉尼科技大学的Zonghan Wu博士,师从IEEE member Shirui Pan,作者还发表了一篇GNN的综述[《A Comprehensive Survey on Graph Neural Networks》](https://arxiv.org/pdf... ...
【时空序列预测第十九篇】时空序列预测模型之GraphWaveNet 一、Address 发表在于IJCAI 2019的一篇文章:Graph WaveNet for Deep Spatial-Temporal Graph Modeling 地址:/pdf/1906.00121.pdf 二、Introduction 时空图模型的一个基本假设是:节点未来信息仅取决于该节点和其邻居的历史信息。后续研究成果证明了将数据的图结构...
Graph WaveNet for Deep Spatial-Temporal Graph Modeling This is the original pytorch implementation of Graph WaveNet in the following paper: [Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019] (https://arxiv.org/abs/1906.00121). A nice improvement over GraphWavenet is presented by...
Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv: 1906.00121 (2019). 38. Zhao, W., Zhang, S., Zhou, B. & Wang, B. STCGAT: Spatial-temporal causal networks for complex urban road traffic flow predic- tion. arXiv preprint arXiv:2203.10749 (...
Graph-WaveNetStormwater drainagePredictive modelingSmart citiesUrban hydrologyGraph Neural Networks (GNNs) have been applied to network data such as traffic flow and water distribution systems, yet their use in predicting the state of urban stormwater drainage systems remains rare. This study investigates...
1、文章信息《Graph WaveNet for Deep Spatial-Temporal Graph Modeling》。这是悉尼科技大学发表在国际顶级会议IJCAI 2019上的一篇文章。这篇文章虽然不是今年的最新成果,但是有一些思想是十分值得借鉴的,所以…