3 TEMPORAL GRAPH NETWORKS 根据(Representation learning for dynamic graphs: A survey)中的观点,动态图的神经模型可以被视为编码器-解码器对,其中编码器是一个函数,从动态图映射到节点嵌入,解码器将一个或多个节点嵌入作为输入,并进行特定于任务的预测,如节点分类或链接预测。本文的主要贡献是一种新颖的时间图网络...
Temporal Graph Sum (sum): 图上更简单,更快速的聚合: 两种训练机制:
To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.Design/methodology/approachThis paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks...
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better 论文链接:https://arxiv.org/abs/1909.10248 0.前言 0.1 现有问题: (1)现实世界的属性图通常是异质的,并且(2)随着时间动态变化 0.2 解决方案: (1) 异质GCN:在每个时间步(time step) 获取每个异质图的特征表示 (2)...
3 TEMPORAL GRAPH NETWORKS 根据(Representation learning for dynamic graphs: A survey)中的观点,动态图的神经模型可以被视为编码器-解码器对,其中编码器是一个函数,从动态图映射到节点嵌入,解码器将一个或多个节点嵌入作为输入,并进行特定于任务的预测,如节点分类或链接预测。本文的主要贡献是一种新颖的时间图网络...
In this paper, we propose Multimodal Temporal Graph Attention Networks (MTGAT). MTGAT is an interpretable graph-based neural model that provides a suitable framework for analyzing this type of multimodal sequential data. We first introduce a procedure to convert unaligned multimodal sequence data ...
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 交通流预测是交通领域研究人员和实践者的一个关键问题。然而,由于交通流通常具有高度的非线性和复杂的模式,这是非常具有挑战性的。现有的大多数交通流预测方法缺乏对交通数据动态时空相关性建模的能力,无法获得满意的预测结果。
Each space–time block is composed of two graph attention networks and a gated recurrent unit, which are used to extract the spatial and temporal characteristics of road traffic flow respectively, while adding residual connections to prevent the gradient from disappearing. Then, with the traffic ...
A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction Deep graph convolution networks (GCNs) have recently shown excellent performance in traffic prediction tasks. However, they face some challenges. First, fe... L Chen,Q Zhao,G Li,...
Attention Based Spatial-Temporal Graph Convolutional Networks ASTGCN模型的总体框架。它由三个具有相同结构的独立组件组成,分别用于对历史数据的最近、日周期和周周期依赖性进行建模。 假设采样频率为每天采样q次,当前时间为t_0,预测窗口大小为T_p,我们截取时间轴上长度为T_h、T_d和T_w的三个时间序列段,分别作为...