The PyTorch implementation of STGCN.STGCN-main.zip

# Spatio-Temporal Graph Convolutional Networks [](https://github.com/hazdzz/STGCN/issues) [](https://github.com/hazdzz/STGCN/network/members) [](https://github.com/hazdzz/STGCN/stargazers) [](./LICENSE) ## About The PyTorch implementation of STGCN was implemented for the paper titled *Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting*. ## Paper https://arxiv.org/abs/1709.04875 ## Citation ``` @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} } ``` ## Related works 1. TCN: [*An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling*](https://arxiv.org/abs/1803.01271) 2. GLU and GTU: [*Language Modeling with Gated Convolutional Networks*](https://arxiv.org/abs/1612.08083) 3. ChebNet: [*Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering*](https://arxiv.org/abs/1606.09375) 4. GCN: [*Semi-Supervised Classification with Graph Convolutional Networks*](https://arxiv.org/abs/1609.02907) ## Related code 1. TCN: https://github.com/locuslab/TCN 2. ChebNet: https://github.com/mdeff/cnn_graph 3. GCN: https://github.com/tkipf/pygcn ## Dataset ### Source 1. METR-LA: [DCRNN author's Google Drive](https://drive.google.com/file/d/1pAGRfzMx6K9WWsfDcD1NMbIif0T0saFC/view?usp=sharing) 2. PEMS-BAY: [DCRNN author's Google Drive](https://drive.google.com/file/d/1wD-mHlqAb2mtHOe_68fZvDh1LpDegMMq/view?usp=sharing) 3. PeMSD7(M): [STGCN author's GitHub repository](https://github.com/VeritasYin/STGCN_IJCAI-18/blob/master/data_loader/PeMS-M.zip) ### Preprocessing Using the formula from [ChebNet](https://arxiv.org/abs/1606.09375)： <img src="./figure/weighted_adjacency_matrix.png" style="zoom:100%" /> ## Model structure <img src="./figure/stgcn_model_structure.png" style="zoom:100%" /> ## Differents of code between mine and author's 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 ## Requirements To install requirements: ```console pip3 install -r requirements.txt ```