The official implementation of the ASGN model. Orginal paper: ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. KDD'2020 Accepted.

• 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}
}