A Machine Learning Approach for Predicting Human Preference for Graph Layouts

TL;DR

This paper introduces a novel machine learning method that predicts human preferences for graph layouts by leveraging transfer learning and quality metrics, achieving successful predictions validated by experimental data.

Contribution

It is the first approach to predict human preferences for graph layouts using a deep learning model trained with transfer learning and quality metrics.

Findings

Model accurately predicts human preferences based on experimental data

Transfer learning improves prediction performance with limited data

Quality metrics correlate with human preferences in graph layouts

Abstract

Understanding what graph layout human prefer and why they prefer is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts. In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are shown to be correlated to human preference on graph layouts. Experimental results using the ground truth human preference data sets show that our model can successfully predict human preference for graph layouts. To our best knowledge, this is the first approach for predicting qualitative evaluation of graph layouts using human preference experiment data.