wehub-resource-sync
78 lines
2.5 KiB
Markdown
78 lines
2.5 KiB
Markdown
# DGL Implementation of ARMA
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This DGL example implements the GNN model proposed in the paper [Graph Neural Networks with convolutional ARMA filters](https://arxiv.org/abs/1901.01343).
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Contributor: [xnuohz](https://github.com/xnuohz)
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### Requirements
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The codebase is implemented in Python 3.6. For version requirement of packages, see below.
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```
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dgl
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numpy 1.19.5
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networkx 2.5
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scikit-learn 0.24.1
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tqdm 4.56.0
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torch 1.7.0
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```
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### The graph datasets used in this example
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###### Node Classification
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The DGL's built-in Cora, Pubmed, Citeseer datasets. Dataset summary:
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| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
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| Cora | 2,708 | 10,556 | 1,433 | 7(single label) | 140 | 500 | 1000 |
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| Citeseer | 3,327 | 9,228 | 3,703 | 6(single label) | 120 | 500 | 1000 |
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| Pubmed | 19,717 | 88,651 | 500 | 3(single label) | 60 | 500 | 1000 |
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### Usage
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###### Dataset options
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```
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--dataset str The graph dataset name. Default is 'Cora'.
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```
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###### GPU options
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```
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--gpu int GPU index. Default is -1, using CPU.
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```
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###### Model options
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```
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--epochs int Number of training epochs. Default is 2000.
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--early-stopping int Early stopping rounds. Default is 100.
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--lr float Adam optimizer learning rate. Default is 0.01.
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--lamb float L2 regularization coefficient. Default is 0.0005.
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--hid-dim int Hidden layer dimensionalities. Default is 16.
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--num-stacks int Number of K. Default is 2.
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--num-layers int Number of T. Default is 1.
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--dropout float Dropout applied at all layers. Default is 0.75.
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```
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###### Examples
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The following commands learn a neural network and predict on the test set.
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Train an ARMA model which follows the original hyperparameters on different datasets.
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```bash
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# Cora:
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python citation.py --gpu 0
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# Citeseer:
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python citation.py --gpu 0 --dataset Citeseer --num-stacks 3
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# Pubmed:
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python citation.py --gpu 0 --dataset Pubmed --dropout 0.25 --num-stacks 1
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```
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### Performance
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###### Node Classification
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| Dataset | Cora | Citeseer | Pubmed |
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| :-: | :-: | :-: | :-: |
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| Metrics(Table 1.Node classification accuracy) | 83.4±0.6 | 72.5±0.4 | 78.9±0.3 |
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| Metrics(PyG) | 82.3±0.5 | 70.9±1.1 | 78.3±0.8 |
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| Metrics(DGL) | 80.9±0.6 | 71.6±0.8 | 75.0±4.2 | |