Revisited experimental comparison of node-link and matrix representations

Mershack Okoe, Radu Jianu, Stephen G Kobourov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Visualizing network data is applicable in domains such as biology, engineering, and social sciences. We report the results of a study comparing the effectiveness of the two primary techniques for showing network data: node-link diagrams and adjacency matrices. Specifically, an evaluation with a large number of online participants revealed statistically significant differences between the two visualizations. Our work adds to existing research in several ways. First, we explore a broad spectrum of network tasks, many of which had not been previously evaluated. Second, our study uses a large dataset, typical of many real-life networks not explored by previous studies. Third, we leverage crowdsourcing to evaluate many tasks with many participants.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 25th International Symposium, GD 2017, Revised Selected Papers
PublisherSpringer-Verlag
Pages287-302
Number of pages16
ISBN (Print)9783319739144
DOIs
StatePublished - Jan 1 2018
Event25th International Symposium on Graph Drawing and Network Visualization, GD 2017 - Boston, United States
Duration: Sep 25 2017Sep 27 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Symposium on Graph Drawing and Network Visualization, GD 2017
CountryUnited States
CityBoston
Period9/25/179/27/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Okoe, M., Jianu, R., & Kobourov, S. G. (2018). Revisited experimental comparison of node-link and matrix representations. In Graph Drawing and Network Visualization - 25th International Symposium, GD 2017, Revised Selected Papers (pp. 287-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10692 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-73915-1_23