Learning With Molecules Beyond Graph Neural Networks
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**arXiv ID:** 2011.03488 **Authors:** Gustav Sourek, Filip Zelezny, Ondrej Kuzelka **Published:** 2020-11-06T17:42:42Z **Abstract:** We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic....
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# Learning with Molecules beyond Graph Neural Networks
**arXiv ID:** 2011.03488
**Authors:** Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
**Published:** 2020-11-06T17:42:42Z
**Abstract:**
We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic. The declarative nature of the used language then allows to easily modify and extend the propagation schemes into complex structures, such as the molecular rings which we choose for a short demonstration in this paper.
## Skill Description
This skill is generated from the arXiv paper: Learning with Molecules beyond Graph Neural Networks (2011.03488).
## How to Use
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## References
- [arXiv:2011.03488](http://arxiv.org/abs/2011.03488v1)
