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Logs First Mar 7, 2019
Synthetic_Data minor changes May 14, 2019

Relational Pooling for Graph Representations


This is the code associated with the paper Relational Pooling for Graph Representations. Accepted at ICML, 2019.

Our first task evaluates RP-GIN, a powerful model we propose to make Graph Isomorphism Network (GIN) Xu et. al. 2019 more powerful than its corresponding WL[1] test. Our second set of tasks uses molecule datasets to evaluate different instantiations of RP.

The models are described in plain English in the appendix of our paper, but feel free to contact us with any questions (see below).


For the first set of tasks, you will need

  • SciPy
  • scikit-learn
  • docopt and schema for parsing arguments from command line

For the molecular tasks, you will need

  • DeepChem and its associated dependencies

Examples: How to Run

  • An example call for the synthetic tasks follows. We trained these models on CPUs. Please see the docstring for further details
python --cv-fold 0 --model-type rpGin --out-weight-dir /some/path --out-log-dir /another/path/ --onehot-id-dim 10
  • Now we show examples for the molecular tasks. The Tox 21 dataset is smaller so we demonstrate with that. For the molecular k-ary tasks:
python 'tox_21' 20
  • For the molecular RP-Duvenaud tasks:
python 'tox_21' 'unique_local' 0
  • For the molecular RNN task:
python 'tox_21'


  • The datasets for the first set of tasks are available in the Synthetic_Data directory.
  • The datasets for the molecular tasks are all available in the DeepChem package.

Questions and Contact

Please feel free to reach out to Ryan Murphy ( if you have any questions.


If you use this code, please consider citing our paper. Here is the Bibtex entry:

  title = 	 {Relational Pooling for Graph Representations},
  author = 	 {Murphy, Ryan and Srinivasan, Balasubramaniam and Rao, Vinayak and Ribeiro, Bruno},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {4663--4673},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Long Beach, California, USA},
  month = 	 {09--15 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {},
  url = 	 {}
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