Graph-level Representation Learning with Joint-Embedding Predictive Architectures
conda create --name graphjepa python=3.8
conda activate graphjepa
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
conda install -c pyg pytorch-sparse
conda install -c pyg pytorch-scatter
conda install -c pyg pytorch-cluster
conda install -c pyg pyg
pip install yacs
pip install tensorboard
pip install networkx
pip install einops
pip install metis
The code for all the available datasets used in the paper is available under the train/ folder. Inside train/configs you can find the specific configuration files used for each dataset, corresponding with Table 8 of the paper. launch.sh contains two examples of launch scripts that you can use to directly modify the default config. You can see a detailed list of the arguments in core/config.py
For the sake of openess, we provide the training logs containing the results published in the paper in paper_logs/
This repository is largely based on Graph-ViT-MLPMixer, check out their work if you are interested in graph representation learning with Transformers.