PyDGN: a research library for Deep Graph Networks

Documentation

This is a Python library to easily experiment with Deep Graph Networks (DGNs). It provides automatic management of data splitting, loading and common experimental settings. It also handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU or GPU).

Reference

If you are interested in a rigorous evaluation of specific Deep Graph Networks, which kick-started this library, check this out:

Errica Federico, Podda Marco, Bacciu Davide, Micheli Alessio: A Fair Comparison of Graph Neural Networks for Graph Classification. Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). Code

Installation:

Simply run

pip install pydgn

Quickstart:

Build dataset and data splits

pydgn-dataset --config-file examples/DATA_CONFIGS/config_NCI1.yml

Train

pydgn-train  --config-file examples/MODEL_CONFIGS/config_SupToyDGN.yml 

And we are up and running!

To debug your code you can add --debug to the command above, but the "GUI" will be disabled.

To stop the computation, use CTRL-C to send a SIGINT signal, and consider using the command ray stop to stop all Ray processes. Warning: ray stop stops all ray processes you have launched, including those of other experiments in progress, if any.

Projects using PyDGN

• Infinite Contextual Graph Markov Model (ICML 2022)
• Graph Mixture Density Networks (ICML 2021)
• Contextual Graph Markov Model (ICML 2018, JMLR 2020)
• Extended Contextual Graph Markov Model (IJCNN 2021)
• Continual Learning Benchmark for Graphs (WWW Workshop 2021, spotlight)

Data Splits

We provide the data splits taken from

Errica Federico, Podda Marco, Bacciu Davide, Micheli Alessio: A Fair Comparison of Graph Neural Networks for Graph Classification. 8th International Conference on Learning Representations (ICLR 2020). Code

in the examples/DATA_SPLITS folder.

License:

PyDGN >= 1.0.0 is BSD 3-Clause licensed, as written in the LICENSE file.