SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals

TL;DR

SmartGD is a novel GAN-based framework for graph drawing that optimizes multiple aesthetic criteria, including non-differentiable ones, demonstrating superior performance over existing algorithms in various aesthetic measures.

Contribution

The paper introduces a flexible GAN-based deep learning framework capable of optimizing diverse aesthetic goals in graph drawing, including non-differentiable criteria.

Findings

Effective in minimizing stress and edge crossings

Maximizes crossing angles and shape-based metrics

Outperforms popular algorithms in quality and efficiency

Abstract

While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of SmartGD, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.