graphesn 0.2.3

Python implementation of Deep Graph Echo State Networks

Graph ESN library

Pytorch implementation of echo state networks for static graphs and discrete-time dynamic graphs.

Content Summmary

• examples/planetoid contains an example of the library usage in the static graph domain.
• src/graphesn/reservoir contains the implementations of the echo state networks for static graphs and discrete-time dynamic graphs.
• src/graphesn/readout contains the implementation of a linear readout.
• src/graphesn/matrix contains useful matrix operations.

Installation

python3 -m pip install graphesn

References

• C. Gallicchio, A. Micheli (2010). Graph Echo State Networks. The 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp. 3967–3974.
• C. Gallicchio, A. Micheli (2020). Fast and Deep Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).
• C. Gallicchio, A. Micheli (2020). Ring Reservoir Neural Networks for Graphs. The 2020 International Joint Conference on Neural Networks (IJCNN 2020).
• D. Tortorella, A. Micheli (2021). Dynamic Graph Echo State Networks. Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), pp. 99–104.

License

graph-esn is MIT licensed, as written in the LICENSE file.

Contributing

This research software is provided as-is. We are working on this library in our spare time.

If you find a bug, please open an issue to report it, and we will do our best to solve it. For generic/technical questions, please email us rather than opening an issue.