1. A task-based evaluation of combined set and network visualization
- Author
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Simon Thompson, Robert Baker, Peter Rodgers, Bilal Alsallakh, Gem Stapleton, Luana Michallef, University of Kent, University of Brighton, Vienna University of Technology, Department of Computer Science, Aalto-yliopisto, and Aalto University
- Subjects
Graph visualization ,Information Systems and Management ,Computer science ,Set visualization ,02 engineering and technology ,Machine learning ,computer.software_genre ,Combined visualization ,050105 experimental psychology ,Clustering ,Theoretical Computer Science ,Task (project management) ,Set (abstract data type) ,Artificial Intelligence ,Graph drawing ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,QA ,Cluster analysis ,Social network analysis ,ta113 ,business.industry ,05 social sciences ,020207 software engineering ,Computer Science Applications ,Control and Systems Engineering ,Data mining ,Artificial intelligence ,Networks ,business ,computer ,Software - 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.
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