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
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
| WikiCS | Am.Computers | Am.Photos | Co.CS | Co.Physics |
|---|---|---|---|---|
| 79.50 | 88.21 | 92.76 | 92.49 | 94.89 |
Codes are borrowed from BYOL and SelfGNN
| name | Implementation Code | Paper |
|---|---|---|
Bootstrap Your Own Latent |
Implementation | paper |
SelfGNN |
Implementation | paper |