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

Oh Hyun Kwon, Tarik Crnovrsanin, Kwan-Liu Ma

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8017580
Pages (from-to)478-488
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • aesthetics
  • graph kernel
  • graph layout
  • Graph visualization
  • graphlet
  • machine learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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