A task-based evaluation of combined set and network visualization

Peter Rodgers, Gem Stapleton, Bilal Alsallakh, Luana Michallef, Rob Baker, Simon Thompson

Research output: Contribution to journalArticlepeer-review


This paper addresses the problem of how best to visualize network datagrouped 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 whichdraw the sets first, yield significantly faster user responses than Bubble Sets, KelpFusion and LineSets, all of which draw the network first.
Original languageEnglish
Pages (from-to)58-79
Number of pages22
JournalInformation Sciences
Publication statusPublished - 2 Jun 2016

Bibliographical note

© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)


  • Set visualization
  • Graph visualization
  • Combined visualization
  • Clustering
  • Networks


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