https://github.com/hazdzz/stgcn

The PyTorch implementation of STGCN.
https://github.com/hazdzz/stgcn

gcn gnn pytorch road-traffic-prediction tcn

Last synced: 3 days ago
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The PyTorch implementation of STGCN.

• Host: GitHub
• URL: https://github.com/hazdzz/stgcn
• Owner: hazdzz
• License: lgpl-2.1
• Created: 2020-11-16T20:36:50.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-12-07T06:27:09.000Z (about 1 month ago)
• Last Synced: 2025-01-08T14:21:55.629Z (10 days ago)
• Topics: gcn, gnn, pytorch, road-traffic-prediction, tcn
• Language: Python
• Homepage:
• Size: 126 MB
• Stars: 513
• Watchers: 6
• Forks: 111
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Spatio-Temporal Graph Convolutional Networks

[![issues](https://img.shields.io/github/issues/hazdzz/STGCN)](https://github.com/hazdzz/STGCN/issues)

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## About

The PyTorch implementation of STGCN from the paper *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)：



## Model structure



## 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

```