A task-based evaluation of combined set and network visualization
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2016-11-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
22
58-79
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
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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
