The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs)...
Spatio-Temporal Graph neural network (STGNN). Trainer Trainer contains every information (such as dataset, optimizer, loss function, etc) for training each type of models mentioned above. Callbacks This module consists of callbacks which can be executed before/after some steps of training or testing...
KienMN / STGNN-for-Covid-in-Korea Star 28 Code Issues Pull requests Spatio-temporal graph neural network for predicting COVID-19 new cases in Korea. timeseries forecasting graph-neural-networks covid-19 spatio-temporal-graphs Updated Dec 8, 2021 Python Kumbong / GraphWavenet Star 0 Co...
DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction... Z Cai,C ...
graph structures from input with spatial dependencies. When both spatiotemporal features are present, spatiotemporal graph neural networks (STGNN) can handle data efficiently. Weiguo Zhu23proposed CorrSTN, a graphical model for traffic flow prediction based on spatiotemporal correlation information. The ...
Therefore, this article considers the characteristics of the near-Earth remote sensing system and proposes a spatio-temporal graph attention network (N-STGAT) that considers the node status, applying spatio-temporal graph neural networks (STGNN) [15] to network intrusion detection in the near-Earth...
As shown in Figure 3a, this layer consists of a heterogeneous graph attention neural network and two temporal gated convolutional neural networks, which are used to process, respectively, the spatial features and temporal features from the neighbor nodes of a meta path on the road network. 4.2....
通用工具和库。OpenSTL 是时空预测学习的基准,涵盖广泛方法和任务。BasicTS 是基于 PyTorch 的基准测试和工具箱,用于时间序列预测。Merlion 是开源机器学习库,支持单变量和多元模型。darts 是专为时间序列预测和异常检测设计的Python库。PyTorch Geometric Temporal 是 PyTorch Geometric 的动态扩展库,支持各种功能。
Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as t...
This research investigates power allocation in wireless device-to-device (D2D) networks using spatio-temporal graph neural networks (STGNNs). Specifically, we address the challenge of sum-rate maximization in D2D networks by formulating it as a reinforcement learning problem. In our approach, STGNNs...