原文:(PDF) Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 现有的交通流预测方法大多缺乏对交通数据的动态时空相关性进行建模的能力,因此无法得到令人满意的预测结果。因此这篇文章提出了一种新的基于注意力的时空图卷积网络(Attentio
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。
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。
In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node....
论文地址:Attention Guided Graph Convolutional Networks for Relation Extraction (aclanthology.org) 核心内容:现有的关系抽取模型可以分为两类:基于序列的关系抽取模型和基于依赖关系的关系抽取模型。基于序列的模型仅仅针对单词序列,而基于依赖关系的模型针对整个由原本文中单词之间关系构建的依赖关系树。因此基于依赖关系...
论文阅读06——《CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering》 Ideas: Model: 交叉注意力融合模块 图自编码器 Ideas: 提出一种基于端到端的交叉注意力融合的深度聚类框架,其中交叉注意力融合模块创造性地将图卷积自编码器模块和自编码器模块多层级连起来 ...
In recent years, graph neural networks have been widely used in spatial information extraction of road networks due to their powerful performance in capturing the structural features of graph networks23. Guo et al.24 proposed a new Attention-based Spatiotemporal graph Convolutional Network (ASTGCN) ...
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms Introduction The complex functions of a living cell are conducted through the concerted activity of many genes and gene products. Much of the activity of a cell is ...
Graph convolutional networks can expand convolution operations to achieve convolutions directly on irregular graph structures. Bruna et al. [19] designed a variant of graph convolution based on spectral theory for the first time. Since then, spectral-based graph convolution networks have been ...
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism (ICCV19) 链接:http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_AttPool_Towards_Hierarchical_Feature_Representation_in_Graph_Convolutional_Networks_via_ICCV_2019_paper.html ...