wehub-resource-sync
111 lines
4.8 KiB
Markdown
111 lines
4.8 KiB
Markdown
# DGL Implementation of BGRL
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This DGL example implements the GNN experiment proposed in the paper [Large-Scale Representation Learning on Graphs via Bootstrapping](https://arxiv.org/abs/2102.06514). For the original implementation, see [here](https://github.com/nerdslab/bgrl).
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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 0.8.3
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numpy 1.21.2
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torch 1.10.2
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scikit-learn 1.0.2
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```
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### Dataset
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Dataset summary:
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| Dataset | Task | Nodes | Edges | Features | Classes |
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|:----------------:|:------------:|:------:|:-------:|:--------:|:---------------:|
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| WikiCS | Transductive | 11,701 | 216,123 | 300 | 10 |
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| Amazon Computers | Transductive | 13,752 | 245,861 | 767 | 10 |
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| Amazon Photos | Transductive | 7,650 | 119,081 | 745 | 8 |
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| Coauthor CS | Transductive | 18,333 | 81,894 | 6,805 | 15 |
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| Coauthor Physics | Transductive | 34,493 | 247,962 | 8,415 | 5 |
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| PPI(24 graphs) | Inductive | 56,944 | 818,716 | 50 | 121(multilabel) |
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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 'amazon_photos'.
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```
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##### Model options
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```
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--graph_encoder_layer list Convolutional layer hidden sizes. Default is [256, 128].
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--predictor_hidden_size int Hidden size of predictor. Default is 512.
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```
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##### Training options
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```
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--epochs int The number of training epochs. Default is 10000.
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--lr float The learning rate. Default is 0.00001.
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--weight_decay float The weight decay. Default is 0.00001.
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--mm float The momentum for moving average. Default is 0.99.
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--lr_warmup_epochs int Warmup period for learning rate scheduling. Default is 1000.
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--weights_dir str Where to save the weights. Default is '../weights'.
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```
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##### Augmentation options
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```
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--drop_edge_p float Probability of edge dropout. Default is [0., 0.].
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--feat_mask_p float Probability of node feature masking. Default is [0., 0.].
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```
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##### Evaluation options
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```
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--eval_epochs int Evaluate every eval_epochs. Default is 250.
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--num_eval_splits int Number of evaluation splits. Default is 20.
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--data_seed int Data split seed for evaluation. Default is 1.
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```
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### Instructions for experiments
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##### Transductive task
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```
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# Coauthor CS
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python main.py --dataset coauthor_cs --graph_encoder_layer 512 256 --drop_edge_p 0.3 0.2 --feat_mask_p 0.3 0.4
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# Coauthor Physics
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python main.py --dataset coauthor_physics --graph_encoder_layer 256 128 --drop_edge_p 0.4 0.1 --feat_mask_p 0.1 0.4
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# WikiCS
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python main.py --dataset wiki_cs --graph_encoder_layer 512 256 --drop_edge_p 0.2 0.3 --feat_mask_p 0.2 0.1 --lr 5e-4
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# Amazon Photos
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python main.py --dataset amazon_photos --graph_encoder_layer 256 128 --drop_edge_p 0.4 0.1 --feat_mask_p 0.1 0.2 --lr 1e-4
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# Amazon Computers
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python main.py --dataset amazon_computers --graph_encoder_layer 256 128 --drop_edge_p 0.5 0.4 --feat_mask_p 0.2 0.1 --lr 5e-4
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```
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##### Inductive task
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```
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# PPI
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python main.py --dataset ppi --graph_encoder_layer 512 512 --drop_edge_p 0.3 0.25 --feat_mask_p 0.25 0. --lr 5e-3
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```
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### Performance
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##### Transductive Task
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| Dataset | WikiCS | Am. Comp. | Am. Photos | Co. CS | Co. Phy |
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|:----------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|
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| Accuracy Reported | 79.98 ± 0.10 | 90.34 ± 0.19 | 93.17 ± 0.30 | 93.31 ± 0.13 | 95.73 ± 0.05 |
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| Accuracy Official Code | 79.94 | 90.62 | 93.45 | 93.42 | 95.74 |
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| Accuracy DGL | 80.00 | 90.64 | 93.34 | 93.76 | 95.79 |
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##### Inductive Task
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| Dataset | PPI |
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|:----------------------:|:------------:|
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| Micro-F1 Reported | 69.41 ± 0.15 |
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| Accuracy Official Code | 68.83 |
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| Micro-F1 DGL | 68.65 |
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##### Accuracy reported is over 20 random dataset splits and model initializations. Micro-F1 reported is over 20 random model initializations.
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##### Accuracy official code and Accuracy DGL is only over 1 random dataset splits and model initialization. Micro-F1 official code and Micro-F1 DGL is only over 1 random model initialization. |