DGL Implementation of BGRL

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

Requirements

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

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)

Usage

Dataset options

--dataset                     str         The graph dataset name.                         Default is 'amazon_photos'.

Model options

--graph_encoder_layer         list        Convolutional layer hidden sizes.               Default is [256, 128].
--predictor_hidden_size       int         Hidden size of predictor.                       Default is 512.

Training options

--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'.

Augmentation options

--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.].

Evaluation options

--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.

Instructions for experiments

Transductive task

# 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

Inductive task

# 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

Performance

Transductive Task

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

Inductive Task

Dataset	PPI
Micro-F1 Reported	69.41 ± 0.15
Accuracy Official Code	68.83
Micro-F1 DGL	68.65

Accuracy reported is over 20 random dataset splits and model initializations. Micro-F1 reported is over 20 random model initializations.

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.