# Deep Neural Network for DrawiNg Networks, (DNN)^2

arXiv: https://arxiv.org/abs/2108.03632 · HTML: https://mlsanity.com/paper/2108.03632/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [A Deep Generative Model for Graph Layout](https://mlsanity.com/paper/1904.12225/) (2019)
- [DeepDrawing: A Deep Learning Approach to Graph Drawing](https://mlsanity.com/paper/1907.11040/) (2019)
- [Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach](https://mlsanity.com/paper/1808.00703/) (2018)
- [Deep Learning Multidimensional Projections](https://mlsanity.com/paper/1902.07958/) (2019)
- [Toward automatic comparison of visualization techniques: Application to graph visualization](https://mlsanity.com/paper/1910.09477/) (2019)
- [Approximating the Spectrum of a Graph](https://mlsanity.com/paper/1712.01725/) (2017)
- [What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization](https://mlsanity.com/paper/1710.04328/) (2017)
- [GraphTSNE: A Visualization Technique for Graph-Structured Data](https://mlsanity.com/paper/1904.06915/) (2019)
- [Graph Drawing via Gradient Descent, (GD)2](https://mlsanity.com/paper/2008.05584/) (2020)

## Built on by (most cited first, 6 of 6)

- [SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals](https://mlsanity.com/paper/2206.06434/) (2022)
- [FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing](https://mlsanity.com/paper/2208.10334/) (2022)
- [Can an NN model plainly learn planar layouts?](https://mlsanity.com/paper/2209.01075/) (2022)
- [An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem](https://mlsanity.com/paper/2508.15949/) (2025)
- [Using Reinforcement Learning to Optimize the Global and Local Crossing Number](https://mlsanity.com/paper/2509.06108/) (2025)
- [Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization](https://mlsanity.com/paper/2604.03417/) (2026)

## Abstract (arXiv)

By leveraging recent progress of stochastic gradient descent methods, several works have shown that graphs could be efficiently laid out through the optimization of a tailored objective function. In the meantime, Deep Learning (DL) techniques achieved great performances in many applications. We demonstrate that it is possible to use DL techniques to learn a graph-to-layout sequence of operations thanks to a graph-related objective function. In this paper, we present a novel graph drawing framework called (DNN)^2: Deep Neural Network for DrawiNg Networks. Our method uses Graph Convolution Networks to learn a model. Learning is achieved by optimizing a graph topology related loss function that evaluates (DNN)^2 generated layouts during training. Once trained, the (DNN)^ model is able to quickly lay any input graph out. We experiment (DNN)^2 and statistically compare it to optimization-based and regular graph layout algorithms. The results show that (DNN)^2 performs well and are encouraging as the Deep Learning approach to Graph Drawing is novel and many leads for future works are identified.