The PyTorch implementation of STGCN from the paper Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

https://arxiv.org/abs/1709.04875

@inproceedings{10.5555/3304222.3304273,
author = {Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},
title = {Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},
year = {2018},
isbn = {9780999241127},
publisher = {AAAI Press},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},
pages = {3634–3640},
numpages = {7},
series = {IJCAI'18}
}

1. TCN: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
2. GLU and GTU: Language Modeling with Gated Convolutional Networks
3. ChebNet: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
4. GCN: Semi-Supervised Classification with Graph Convolutional Networks

1. TCN: https://github.com/locuslab/TCN
2. ChebNet: https://github.com/mdeff/cnn_graph
3. GCN: https://github.com/tkipf/pygcn

1. METR-LA: DCRNN author's Google Drive
2. PEMS-BAY: DCRNN author's Google Drive
3. PeMSD7(M): STGCN author's GitHub repository

Using the formula from ChebNet：

1. Fix bugs
2. Add Early Stopping approach
3. Add Dropout approach
4. Offer a different set of hyperparameters
5. Offer config files for two different categories graph convolution (ChebyGraphConv and GraphConv)
6. Add datasets METR-LA and PEMS-BAY
7. Adopt a different data preprocessing method

To install requirements:

pip3 install -r requirements.txt