In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate...
3 TEMPORAL GRAPH ATTENTION NETWORK ARCHITECTURE 3.1 FUNCTIONAL TIME ENCODING 在这一节,目标是为了将时间通过某种映射,将时间从时域映射到数域(用嵌入向量表示)\Phi: T \rightarrow \mathbb{R}^{d_{T}},以用来代替位置嵌入(等式1中的位置编码)做自注意力权重计算。一般来说,我们假设时间域可以用从原点开始的...
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(Attention Based Spatial-Temporal Graph Convolutional Network, ASTGCN)模型来解决交通流预测问题。 ASTGCN主要由三个独立的组件组成,分别对交通流的三个时间属性进行建模,即最近、日周期和周周期依赖关系。更具体地说,每个组件包含两个主要部分:1)有效捕捉交通...
3 TEMPORAL GRAPH ATTENTION NETWORK ARCHITECTURE 3.1 FUNCTIONAL TIME ENCODING 在这一节,目标是为了将时间通过某种映射,将时间从时域映射到数域(用嵌入向量表示)Φ:T→RdTΦ:T→RdT,以用来代替位置嵌入(等式1中的位置编码)做自注意力权重计算。一般来说,我们假设时间域可以用从原点开始的区间TT表示:T=[0,tmax]...
In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive ...
Dynamic Spatial Graph Convolution Network 我们利用学习到的动态空间结构对基于扩散GCN的过程进行改进,从而捕获动态空间关系。这个新模块也被称为动态空间GCN (DSGCN)。 我们最终利用FFN来增强Dynamic GCN的表达能力 实验 两个公共开源的交通数据集,其统计数据如表: ...
In this paper, we propose a novel deep learning framework, Spatial-Temporal Graph Attention Networks (ST-GAT). A graph attention mechanism is adopted to extract the spatial dependencies among road segments. Additionally, we introduce a LSTM network to extract temporal domain features. Compared with...
Graph convolution network(GCN) [21] successfully learns node representation using a localized first-order approximation of spectral graph convolutions. Graph attention network (GAT) [22] introduces the attention mechanism, which collects data from nearby nodes using adaptive weights. Recently, GNNs have...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
Facebook Ego Network(Leskovec and Krevl 2014) Arxiv HEP-TH (Leskovec and Krevl 2014) Synthetic Networks (Watts-Strogatz (Newman 2003) random networks) 是否有开源代码:有(github.com/farzana0/Evo)22. TemporalGAT: Attention-Based Dynamic Graph Representation Learning ...