# What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization

arXiv: https://arxiv.org/abs/1710.04328 · HTML: https://mlsanity.com/paper/1710.04328/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
- [Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction](https://mlsanity.com/paper/2303.09590/) (2023)
- [A Deep Generative Model for Reordering Adjacency Matrices](https://mlsanity.com/paper/2110.04971/) (2021)
- [Interactive Visual Pattern Search on Graph Data via Graph Representation Learning](https://mlsanity.com/paper/2202.09459/) (2022)
- [GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks](https://mlsanity.com/paper/2406.04548/) (2024)

## Abstract (arXiv)

Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.