This repository is the official implementation of paper: "Evaluating Self-supervised Learning for Molecular Graph Embeddings", NeurIPS 2023, Datasets and Benchmarks Track.
@inproceedings{GraphEval, title = {Evaluating Self-supervised Learning for Molecular Graph Embeddings}, author = {Hanchen Wang* and Jean Kaddour* and Shengchao Liu and Jian Tang and Joan Lasenby and Qi Liu}, booktitle = {NeurIPS 2023, Datasets and Benchmarks Track}, year = 2023 }
We include scripts for pre-training, probing and fine-tuning for GraphSSL on molecules, see script folder. We use conda to set up the environment:
conda env create -f env.yaml