A task-based evaluation of combined set and network visualization

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)58-79
Number of pages22
JournalInformation Sciences
Publication statusPublished - 1 Nov 2016
MoE publication typeA1 Journal article-refereed


  • Peter Rodgers
  • Gem Stapleton
  • Bilal Alsallakh
  • Luana Micallef

  • Rob Baker
  • Simon Thompson

Research units

  • University of Kent
  • University of Brighton
  • Vienna University of Technology


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.

    Research areas

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

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