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【索引】图神经论文之GCN（持更）

2022-08-08 13:34:00 【panbaoran913】

GNN中三种基础神经网络：GCN, GAT, GraphSAGE.代表了3种聚合的思想。

这是我的个人理解(不权威)。然随着图神经网络的快速发展，在图神经网络种添加各种组件、搭建新结构或着引入新方法等越来越多，逐渐令人眼花缭乱。特此，在这里整理相关论文，加以梳理，使脉络清晰。提醒：这里的论文大多涉及交通领域。

简称	文章	作用	备注
	Diffusion convolutional recurrent neural network: Data-driven traffic forecasting	扩散图卷积
STGCN	Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting	GCN捕捉空间信息，门控捕捉时间信息	我的《代码分析文集》
	Dynamic spatiotemporal graph convolutional neural networks for traffic data imputation with complex missing patterns	动态图学习？	原文

简称	文章	作用	备注
CAFMGCN	Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction	捕捉3个时间段的数据，捕捉3个不同语义的邻接矩阵,然后使用T-atten和S-atten交叉捕捉信息并融合	只有data,No code
	Joint predictions of multi-modal ride-hailingdemands: A deep multi-task multi-graph learning-based approach		原文

简称	文章	作用	备注
	Deep spatial–temporal sequence modeling for multi-step passenger demand prediction	使用的输入数据为历史需求+气象数据+时间元，预定义矩阵和自适应矩阵搭建的GCN直接合并。采用encoder-decoder结构。decoder是LSTM并用于进行多步预测。	我的博文

$MAE=\frac{1}{n}\sum_{i=1}^n \lvert \hat{y_i}-y_i\rvert$

$RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^n (\hat{y_i}-y_i)}$

$MAPE=\frac{1}{n}\sum_{i=1}^n \lvert \frac{\hat{y_i}-y_i}{y_i}\rvert$

$sMAPE=\frac{200}{n}\sum_{i=1}^{n}\lvert \frac{x_i-y_i}{x_i+y_i} \rvert$

论文名称	简称	损失函数	备注
Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index	DeepTRI	MSE\RMSE\MAPE	我博
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns	DSTGCN	MAE\RMSE	我博
Adaptive Multi-channel Graph Convolutional	AM-GCN	ACC\F1	我博
A deep-learning model for urban traffic flow prediction with traffic events mined from twitter		MAE\RMSE\sMAPE	我博
Meta Graph Transformer: A Novel Framework for Spatial–Temporal Traffic Prediction	MGT	RMSE\MAE\MAPE	我博
Deep spatial–temporal sequence modeling for multi-step passenger demand prediction		RMSE\MAE\MAPE	我博
elf-attention based generative adversarial network for traffic flow prediction	SATP-GAN	MAE\MSE\RMSE	我博
Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach	ST-MRGNN	RMSE\MAE\R^2	我博
Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction	CAFGCN	MAE\RMSE\MAPE	我博
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting	ST-MGCN
Bike Flow Prediction with Multi-Graph Convolutional Networks	MGCN	RMSE	我博
RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems		RMSE\MAE	我博