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main.py | 1 year ago | |
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This DGL example implements the GNN experiment proposed in the paper Large-Scale Representation Learning on Graphs via Bootstrapping. For the original implementation, see here.
Contributor: RecLusIve-F
The codebase is implemented in Python 3.8. For version requirement of packages, see below.
dgl 0.8.3
numpy 1.21.2
torch 1.10.2
scikit-learn 1.0.2
Dataset summary:
Dataset | Task | Nodes | Edges | Features | Classes |
---|---|---|---|---|---|
WikiCS | Transductive | 11,701 | 216,123 | 300 | 10 |
Amazon Computers | Transductive | 13,752 | 245,861 | 767 | 10 |
Amazon Photos | Transductive | 7,650 | 119,081 | 745 | 8 |
Coauthor CS | Transductive | 18,333 | 81,894 | 6,805 | 15 |
Coauthor Physics | Transductive | 34,493 | 247,962 | 8,415 | 5 |
PPI(24 graphs) | Inductive | 56,944 | 818,716 | 50 | 121(multilabel) |
--dataset str The graph dataset name. Default is 'amazon_photos'.
--graph_encoder_layer list Convolutional layer hidden sizes. Default is [256, 128].
--predictor_hidden_size int Hidden size of predictor. Default is 512.
--epochs int The number of training epochs. Default is 10000.
--lr float The learning rate. Default is 0.00001.
--weight_decay float The weight decay. Default is 0.00001.
--mm float The momentum for moving average. Default is 0.99.
--lr_warmup_epochs int Warmup period for learning rate scheduling. Default is 1000.
--weights_dir str Where to save the weights. Default is '../weights'.
--drop_edge_p float Probability of edge dropout. Default is [0., 0.].
--feat_mask_p float Probability of node feature masking. Default is [0., 0.].
--eval_epochs int Evaluate every eval_epochs. Default is 250.
--num_eval_splits int Number of evaluation splits. Default is 20.
--data_seed int Data split seed for evaluation. Default is 1.
# Coauthor CS
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
# Coauthor Physics
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
# WikiCS
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
# Amazon Photos
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
# Amazon Computers
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
# PPI
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
Dataset | WikiCS | Am. Comp. | Am. Photos | Co. CS | Co. Phy |
---|---|---|---|---|---|
Accuracy Reported | 79.98 ± 0.10 | 90.34 ± 0.19 | 93.17 ± 0.30 | 93.31 ± 0.13 | 95.73 ± 0.05 |
Accuracy Official Code | 79.94 | 90.62 | 93.45 | 93.42 | 95.74 |
Accuracy DGL | 80.00 | 90.64 | 93.34 | 93.76 | 95.79 |
Dataset | PPI |
---|---|
Micro-F1 Reported | 69.41 ± 0.15 |
Accuracy Official Code | 68.83 |
Micro-F1 DGL | 68.65 |
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