A positive (a) and a negative (b) GLoRa examples for d = 6: green, blue, and orange nodes have embeddings of form [1, -] (source node), [-, 1] (normal node), and [-, 0] (hole) respectively; T marks the target node; and the red-arrows chain is the path justifying the long-range dependency.

This repository contains the code required to reproduce the experimental results for the architectures described in the "GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs" paper.

Configured for Linux.

conda create -n glora-env python=3.9
conda activate glora-env
conda install pytorch=1.9.1 torchvision torchaudio -c pytorch -c nvidia   
conda install pyg=2.0.2 -c pyg -c conda-forge
pip install ogb
pip install performer-pytorch
pip install torchmetrics==0.7.2
conda install pandas scikit-learn
conda install openbabel fsspec rdkit -c conda-forge
pip install tensorboard
pip install tensorboardX
# Check https://www.dgl.ai/pages/start.html to install DGL based on your CUDA requirements
pip install dgl-cu111 -f https://data.dgl.ai/wheels/repo.html
conda install setuptools==58.0.4
pip install numba
pip install tabulate

From inside configs/paper_configs/GLoRa use:

bash run_dataset_generation_GLoRa.sh

From inside configs/paper_configs/GLoRa use:

bash run_repeat_exps_GLoRa.sh

use:

python main.py --cfg {path_to_yaml_file}

All config files for GLoRa experiments used in the paper are in configs/paper_configs/GLoRa

Use custom arguments by appending the above with {arg_name} {arg_val}. See config files for arg options. E.g. to change the training batch size append "train.batch_size 32"