A task-based evaluation of combined set and network visualization

Loading...
Thumbnail Image

Access rights

openAccess

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2016-11-01

Major/Subject

Mcode

Degree programme

Language

en

Pages

22
58-79

Series

Information Sciences, Volume 367-368

Abstract

This paper addresses the problem of how best to visualize network data grouped into overlapping sets. We address it by evaluating various existing techniques alongside a new technique. Such data arise in many areas, including social network analysis, gene expression data, and crime analysis. We begin by investigating the strengths and weakness of four existing techniques, namely Bubble Sets, EulerView, KelpFusion, and LineSets, using principles from psychology and known layout guides. Using insights gained, we propose a new technique, SetNet, that may overcome limitations of earlier methods. We conducted a comparative crowdsourced user study to evaluate all five techniques based on tasks that require information from both the network and the sets. We established that EulerView and SetNet, both of which draw the sets first, yield significantly faster user responses than Bubble Sets, KelpFusion and LineSets, all of which draw the network first.

Description

Keywords

Clustering, Combined visualization, Graph visualization, Networks, Set visualization

Other note

Citation

Rodgers, P, Stapleton, G, Alsallakh, B, Micallef, L, Baker, R & Thompson, S 2016, ' A task-based evaluation of combined set and network visualization ', Information Sciences, vol. 367-368, pp. 58-79 . https://doi.org/10.1016/j.ins.2016.05.045