wehub-resource-sync
53 lines
1.4 KiB
Markdown
53 lines
1.4 KiB
Markdown
# DGL Implementations of P-GNN
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This DGL example implements the GNN model proposed in the paper [Position-aware Graph Neural Networks](http://proceedings.mlr.press/v97/you19b/you19b.pdf). For the original implementation, see [here](https://github.com/JiaxuanYou/P-GNN).
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Contributor: [RecLusIve-F](https://github.com/RecLusIve-F)
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## Requirements
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The codebase is implemented in Python 3.8. For version requirement of packages, see below.
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```
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dgl 0.7.2
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numpy 1.21.2
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torch 1.10.1
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networkx 2.6.3
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scikit-learn 1.0.2
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```
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## Instructions for experiments
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### Link prediction
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```bash
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# Communities-T
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python main.py --task link
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# Communities
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python main.py --task link --inductive
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```
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### Link pair prediction
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```bash
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# Communities
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python main.py --task link_pair --inductive
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```
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## Performance
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### Link prediction (Grid-T and Communities-T refer to the transductive learning setting of Grid and Communities)
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| Dataset | Communities-T | Communities |
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| :------------------------------: | :-----------: | :-----------: |
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| ROC AUC ( P-GNN-E-2L in Table 1) | 0.988 ± 0.003 | 0.985 ± 0.008 |
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| ROC AUC (DGL: P-GNN-E-2L) | 0.984 ± 0.010 | 0.991 ± 0.004 |
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### Link pair prediction
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| Dataset | Communities |
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| :------------------------------: | :---------: |
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| ROC AUC ( P-GNN-E-2L in Table 1) | 1.0 ± 0.001 |
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| ROC AUC (DGL: P-GNN-E-2L) | 1.0 ± 0.000 |
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