Bootstrapped Representation Learning on Graphs

This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs

The main scripts are train_transductive.py and train_ppi.py used for training on the transductive task datasets and the PPI dataset respectively.

For linear evaluation, using the checkpoints we provide

Setup

To set up a Python virtual environment with the required dependencies, run:

python3 -m venv bgrl_env
source bgrl_env/bin/activate
pip install --upgrade pip

Follow instructions to install PyTorch 1.9.1 and PyG:

pip install torch==1.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
pip install absl-py==0.12.0 tensorboard==2.6.0 ogb

The code uses PyG (PyTorch Geometric). All datasets are available through this package.

Experiments on transductive tasks

Train model from scratch

To run BGRL on a dataset from the transductive setting, use train_transductive.py and one of the configuration files that can be found in config/.

For example, to train on the Coauthor-CS dataset, use the following command:

python3 train_transductive.py --flagfile=config/coauthor-cs.cfg

Flags can be overwritten:

python3 train_transductive.py --flagfile=config/coauthor-cs.cfg\
                              --logdir=./runs/coauthor-cs-256\
                              --predictor_hidden_size=256

Evaluation is performed periodically during training. We fit a logistic regression model on top of the representation to assess its performance throughout training. Evaluation is triggered every eval_epochsand will not back-propagate any gradient to the encoder.

Test accuracies under linear evaluation are reported on TensorBoard. To start the tensorboard server run the following command:

tensorboard --logdir=./runs

Perform linear evaluation using the provided model weights

The configuration files we provide allow to reproduce the results in the paper, summarized in the table below. We also provide weights of the BGRL-trained encoders for each dataset.

	WikiCS	Amazon Computers	Amazon Photos	CoauthorCS	CoauthorPhy
BGRL	79.98 ± 0.10 (weights)	90.34 ± 0.19 (weights)	93.17 ± 0.30 (weights)	93.31 ± 0.13 (weights)	95.73 ± 0.05 (weights)

To run linear evaluation, using the provided weights, run the following command for any of the datasets:

python3 linear_eval_transductive.py --flagfile=config-eval/coauthor-cs.cfg

Note that the dataset is split randomly between train/val/test, so the reported accuracy might be slightly different with each run. In our reported table, we average across multiple splits, as well as multiple randomly initialized network weights.

Experiments on inductive task with multiple graphs

To train on the PPI dataset, use train_ppi.py:

python3 train_ppi.py --flagfile=config/ppi.cfg

The evaluation for PPI is different due to the size of the dataset, we evaluate by training a linear layer on top of the representations via gradient descent for 100 steps.

The configuration files for the different architectures can be found in config/. We provide weights of the BGRL-trained encoder as well.

	PPI
BGRL	69.41 ± 0.15 (weights)

To run linear evaluation, using the provided weights, run the following command:

python3 linear_eval_ppi.py --flagfile=config-eval/ppi.cfg

Note that our reported score is based on an average over multiple runs.

Citation

If you find the code useful for your research, please consider citing our work:

@misc{thakoor2021bootstrapped,
     title={Large-Scale Representation Learning on Graphs via Bootstrapping}, 
     author={Shantanu Thakoor and Corentin Tallec and Mohammad Gheshlaghi Azar and Mehdi Azabou and Eva L. Dyer and Rémi Munos and Petar Veličković and Michal Valko},
     year={2021},
     eprint={2102.06514},
     archivePrefix={arXiv},
     primaryClass={cs.LG}}