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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