BGRL_Pytorch

Implementation of Large-Scale Representation Learning on Graphs via Bootstrapping.

A PyTorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping" paper, accepted in ICLR 2021 Workshop

Hyperparameters for training BGRL

Following Options can be passed to train.py

--layers: or -l: one or more integer values specifying the number of units for each GNN layer. Default is 512 256.
usage example :--layers 512 256

--aug_params: or -p: four float values specifying the hyperparameters for graph augmentation (p_f1, p_f2, p_e1, p_e2). Default is 0.2 0.1 0.2 0.3.
usage example : --aug_params 0.2 0.1 0.2 0.3

params	WikiCS	Am.Computers	Am.Photos	Co.CS	Co.Physics
p_f1	0.2	0.2	0.1	0.3	0.1
p_f2	0.1	0.1	0.2	0.4	0.4
p_e1	0.2	0.5	0.4	0.3	0.4
p_e2	0.3	0.4	0.1	0.2	0.1
embedding size	256	128	256	256	128
encoder hidden size	512	256	512	512	256
predictor hidden size	512	512	512	512	512

• Hyperparameters are from original paper

Experimental Results

WikiCS	Am.Computers	Am.Photos	Co.CS	Co.Physics
79.50	88.21	92.76	92.49	94.89

Codes borrowed from

Codes are borrowed from BYOL and SelfGNN

name	Implementation Code	Paper
Bootstrap Your Own Latent	Implementation	paper
SelfGNN	Implementation	paper