Scalability of network visualisation from a cognitive load perspective

Vahan Yoghourdjian, Yalong Yang, Lee Lawrence, Tim Dwyer, Michael Wybrow, Kim Marriott

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Node-link diagrams are widely used to visualise networks. However, even the best network layout algorithms ultimately result in 'hairball' visualisations when the graph reaches a certain degree of complexity, requiring simplification through aggregation or interaction (such as filtering) to remain usable. Until now, there has been little data to indicate at what level of complexity node-link diagrams become ineffective or how visual complexity affects cognitive load. To this end, we conducted a controlled study to understand workload limits for a task that requires a detailed understanding of the network topology-finding the shortest path between two nodes. We tested performance on graphs with 25 to 175 nodes with varying density. We collected performance measures (accuracy and response time), subjective feedback, and physiological measures (EEG, pupil dilation, and heart rate variability). To the best of our knowledge this is the first network visualisation study to include physiological measures. Our results show that people have significant difficulty finding the shortest path in high density node-link diagrams with more than 50 nodes and even low density graphs with more than 100 nodes. From our collected EEG data we observe functional differences in brain activity between hard and easy tasks. We found that cognitive load increased up to certain level of difficulty after which it decreased, likely because participants had given up. We also explored the effects of global network layout features such as size or number of crossings, and features of the shortest path such as length or straightness on task difficulty. We found that global features generally had a greater impact than those of the shortest path.

Original languageEnglish
Pages (from-to)1677-1687
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number2
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Cognitive Load
  • Data Visualisation
  • EEG
  • Network Visualisation

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