An information-theoretic framework for evaluating edge bundling visualization

Jieting Wu, Feiyu Zhu, Xin Liu, Hongfeng Yu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number625
JournalEntropy
Volume20
Issue number9
DOIs
StatePublished - Aug 21 2018

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layouts
information theory
ambiguity
evaluation

Keywords

  • Edge bundling
  • Graph visualization
  • Information theory
  • Information visualization
  • Minimum description length

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

An information-theoretic framework for evaluating edge bundling visualization. / Wu, Jieting; Zhu, Feiyu; Liu, Xin; Yu, Hongfeng.

In: Entropy, Vol. 20, No. 9, 625, 21.08.2018.

Research output: Contribution to journalArticle

Wu, Jieting ; Zhu, Feiyu ; Liu, Xin ; Yu, Hongfeng. / An information-theoretic framework for evaluating edge bundling visualization. In: Entropy. 2018 ; Vol. 20, No. 9.
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