Next Article in Journal
A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm
Next Article in Special Issue
Dissecting Deep Learning Networks—Visualizing Mutual Information
Previous Article in Journal
Effects of Nitrogen Content on the Structure and Mechanical Properties of (Al0.5CrFeNiTi0.25)Nx High-Entropy Films by Reactive Sputtering
Previous Article in Special Issue
Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets
Open AccessArticle

An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

Department of Computer Science & Engineering, University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, USA
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(9), 625; https://doi.org/10.3390/e20090625
Received: 1 July 2018 / Revised: 14 August 2018 / Accepted: 18 August 2018 / Published: 21 August 2018
(This article belongs to the Special Issue Information Theory Application in Visualization)
Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented. View Full-Text
Keywords: information visualization; graph visualization; edge bundling; information theory; minimum description length information visualization; graph visualization; edge bundling; information theory; minimum description length
Show Figures

Figure 1

MDPI and ACS Style

Wu, J.; Zhu, F.; Liu, X.; Yu, H. An Information-Theoretic Framework for Evaluating Edge Bundling Visualization. Entropy 2018, 20, 625.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop