wehub-resource-sync
86 lines
2.8 KiB
Markdown
86 lines
2.8 KiB
Markdown
# DGL Implementation of JKNet
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This DGL example implements the GNN model proposed in the paper [Representation Learning on Graphs with Jumping Knowledge Networks](https://arxiv.org/abs/1806.03536).
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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 0.6.0
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scikit-learn 0.24.1
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tqdm 4.56.0
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torch 1.7.1
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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, 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) | 60% | 20% | 20% |
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| Citeseer | 3,327 | 9,228 | 3,703 | 6(single label) | 60% | 20% | 20% |
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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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--run int Number of running times. Default is 10.
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--epochs int Number of training epochs. Default is 500.
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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 32.
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--num-layers int Number of T. Default is 5.
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--mode str Type of aggregation ['cat', 'max', 'lstm']. Default is 'cat'.
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--dropout float Dropout applied at all layers. Default is 0.5.
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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 a JKNet which follows the original hyperparameters on different datasets.
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```bash
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# Cora:
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python main.py --gpu 0 --mode max --num-layers 6
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python main.py --gpu 0 --mode cat --num-layers 6
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python main.py --gpu 0 --mode lstm --num-layers 1
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# Citeseer:
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python main.py --gpu 0 --dataset Citeseer --mode max --num-layers 1
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python main.py --gpu 0 --dataset Citeseer --mode cat --num-layers 1
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python main.py --gpu 0 --dataset Citeseer --mode lstm --num-layers 2
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```
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### Performance
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**As the author does not release the code, we don't have the access to the data splits they used.**
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###### Node Classification
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* Cora
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| | JK-Maxpool | JK-Concat | JK-LSTM |
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| :-: | :-: | :-: | :-: |
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| Metrics(Table 2) | 89.6±0.5 | 89.1±1.1 | 85.8±1.0 |
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| Metrics(DGL) | 86.1±1.5 | 85.1±1.6 | 84.2±1.6 |
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* Citeseer
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| | JK-Maxpool | JK-Concat | JK-LSTM |
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| :-: | :-: | :-: | :-: |
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| Metrics(Table 2) | 77.7±0.5 | 78.3±0.8 | 74.7±0.9 |
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| Metrics(DGL) | 70.9±1.9 | 73.0±1.5 | 69.0±1.7 | |