# DeepGD: A Deep Learning Framework for Graph Drawing Using GNN

arXiv: https://arxiv.org/abs/2106.15347 · HTML: https://mlsanity.com/paper/2106.15347/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [VERSE: Versatile Graph Embeddings from Similarity Measures](https://mlsanity.com/paper/1803.04742/) (2018)
- [SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions](https://mlsanity.com/paper/1912.12355/) (2019)
- [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)
- [Aesthetic Discrimination of Graph Layouts](https://mlsanity.com/paper/1809.01017/) (2018)

## Built on by (most cited first, 10 of 10)

- [GrOVe: Ownership Verification of Graph Neural Networks using Embeddings](https://mlsanity.com/paper/2304.08566/) (2023)
- [Graph Neural Networks for Graph Drawing](https://mlsanity.com/paper/2109.10061/) (2021)
- [SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals](https://mlsanity.com/paper/2206.06434/) (2022)
- [CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs](https://mlsanity.com/paper/2402.06706/) (2024)
- [FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing](https://mlsanity.com/paper/2208.10334/) (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)
- [Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities](https://mlsanity.com/paper/2603.05542/) (2026)
- [Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization](https://mlsanity.com/paper/2604.03417/) (2026)
- [Inductive Graph Layout with Implicit Neural Fields](https://mlsanity.com/paper/2608.08876/) (2026)

## Abstract (arXiv)

In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different layout methods tend to highlight different characteristics of the graphs. Recently, studies on deep learning based graph drawing algorithm have emerged but they are often not generalizable to arbitrary graphs without re-training. In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained. It attempts to generate layouts by compromising among multiple pre-specified aesthetics considering a good graph layout usually complies with multiple aesthetics simultaneously. In order to balance the trade-off, we propose two adaptive training strategies which adjust the weight factor of each aesthetic dynamically during training. The quantitative and qualitative assessment of DeepGD demonstrates that it is capable of drawing arbitrary graphs effectively, while being flexible at accommodating different aesthetic criteria.