wehub-resource-sync
72 lines
1.8 KiB
Markdown
72 lines
1.8 KiB
Markdown
# Discrete Temporal Dynamic Graph with recurrent structure
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## DGL Implementation of DCRNN and GaAN paper.
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This DGL example implements the GNN model proposed in the paper [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926) and [GaAN:Gated Attention Networks for Learning on Large and Spatiotemporal Graphs](https://arxiv.org/pdf/1803.07294).
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Model implementor
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----------------------
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This example was implemented by [Ericcsr](https://github.com/Ericcsr) during his Internship work at the AWS Shanghai AI Lab.
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The graph dataset used in this example
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METR-LA dataset. Dataset summary:
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- NumNodes: 207
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- NumEdges: 1722
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- NumFeats: 2
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- TrainingSamples: 70%
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- ValidationSamples: 20%
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- TestSamples: 10%
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PEMS-BAY dataset. Dataset Summary:
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- NumNodes: 325
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- NumEdges: 2694
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- NumFeats: 2
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- TrainingSamples: 70%
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- ValidationSamples: 20%
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- TestSamples: 10%
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How to run example files
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In the dtdg folder, run
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**Please use `train.py`**
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Train the DCRNN model on METR-LA Dataset
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```python
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python train.py --dataset LA --model dcrnn
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```
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If want to use a GPU, run
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```python
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python train.py --gpu 0 --dataset LA --model dcrnn
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```
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if you want to use PEMS-BAY dataset
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```python
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python train.py --gpu 0 --dataset BAY --model dcrnn
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```
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Train GaAN model
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```python
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python train.py --gpu 0 --model gaan --dataset <LA/BAY>
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```
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Performance on METR-LA
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-------------------------
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| Models/Datasets | Test MAE |
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| :-------------- | --------:|
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| DCRNN in DGL | 2.91 |
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| DCRNN paper | 3.17 |
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| GaAN in DGL | 3.20 |
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| GaAN paper | 3.16 |
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Notice that Any Graph Convolution module can be plugged into the recurrent discrete temporal dynamic graph template to test performance; simply replace DiffConv or GaAN.
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