The official implementation of the ASGN model. Orginal paper: ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. KDD'2020 Accepted.
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base_model: Containing SchNet and training code for QM9 and OPV datasets. -
rd_learn: A baseline using random data selection. -
geo_learn: Geometric method of active learning like k_center. -
qbc_learn: Active learning by using query by committee. -
utils: Dataset preparation and utils functions. -
baselines: Active learning baselines from google's implementation. -
single_model_al: contains several baseline models and our method ASGN (in file wsl_al.py) -
exp: Experiments loggings.
If you use ASGN in your research, please use the following BibTex.
@inproceedings{hao2020asgn,
title={ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction},
author={Hao, Zhongkai and Lu, Chengqiang and Huang, Zhenya and Wang, Hao and Hu, Zheyuan and Liu, Qi and Chen, Enhong and Lee, Cheekong},
booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={731--752},
year={2020}
}