# STGCN

**Repository Path**: hazdzz/STGCN

## Basic Information

- **Project Name**: STGCN
- **Description**: The PyTorch implementation of STGCN.
- **Primary Language**: Python
- **License**: LGPL-2.1
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 18
- **Forks**: 13
- **Created**: 2021-03-27
- **Last Updated**: 2025-03-11

## Categories & Tags

**Categories**: ai

**Tags**: None

## README

# Spatio-Temporal Graph Convolutional Networks
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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)：
<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
```