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Understanding node-link and matrix visualizations of networks: A large-scale online experiment

Published online by Cambridge University Press:  05 August 2019

Donghao Ren
Affiliation:
University of California, Santa Barbara, CA 93106, USA
Laura R. Marusich
Affiliation:
U.S. Army Research Laboratory South at the University of Texas at Arlington, TX 76019, USA (e-mail: laura.m.cooper20.civ@mail.mil)
John O’Donovan
Affiliation:
University of California, Santa Barbara, CA 93106, USA (e-mail: jod@cs.ucsb.edu)
Jonathan Z. Bakdash
Affiliation:
U.S. Army Research Laboratory South at the University of Texas at Dallas, TX 75080, USA (e-mail: jonathan.z.bakdash.civ@mail.mil)
James A. Schaffer
Affiliation:
Sysco Labs, Sysco Corporation, Houston, TX 77077, USA (e-mail: j.au.schaffer@gmail.com)
Daniel N. Cassenti
Affiliation:
U.S. Army Research Laboratory, Adelphi, MD 20783, USA (e-mail: daniel.n.cassenti.civ@mail.mil)
Sue E. Kase
Affiliation:
U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA (e-mail: sue.e.kase.civ@mail.mil)
Heather E. Roy
Affiliation:
U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA (e-mail: heather.e.roy2.ctr@mail.mil)
Wan-yi (Sabrina) Lin
Affiliation:
IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA (e-mail: sabrinal@us.ibm.com)
Tobias Höllerer
Affiliation:
University of California, Santa Barbara, CA 93106, USA (e-mail: holl@cs.ucsb.edu)

Abstract

We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/.

Type
Original Article
Copyright
© Cambridge University Press 2019 

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Footnotes

The author is currently affiliated with BOSCH Center for Artificial Intelligence. Email: Wan-Yi.Lin@us.bosch.com

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