# deepstruct -- linking deep learning and graph theory

arXiv: https://arxiv.org/abs/2111.06679 · HTML: https://mlsanity.com/paper/2111.06679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [A Survey on Neural Architecture Search](https://mlsanity.com/paper/1905.01392/) (2019)
- [Structural Analysis of Sparse Neural Networks](https://mlsanity.com/paper/1910.07225/) (2019)
- [Experiments on Properties of Hidden Structures of Sparse Neural Networks](https://mlsanity.com/paper/2107.12917/) (2021)

## Abstract (arXiv)

deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models. For this, deepstruct provides deep neural network models with different restrictions which can be created based on an initial graph. Further, tools to extract graph structures from trained models are available. This step of extracting graphs can be computationally expensive even for models of just a few dozen thousand parameters and poses a challenging problem. deepstruct supports research in pruning, neural architecture search, automated network design and structure analysis of neural networks.