Evaluating perceptually complementary views for network exploration tasks

Chunlei Chang, Benjamin Bach, Tim Dwyer, Kim Marriott

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)


We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.

Original languageEnglish
Title of host publicationProceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
EditorsCliff Lampe, m.c. schraefel
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Print)9781450346559
Publication statusPublished - 2017
EventInternational Conference on Human Factors in Computing Systems 2017 - Colorado Convention Center, Denver, United States of America
Duration: 6 May 201711 May 2017
Conference number: 35th
https://dl.acm.org/doi/proceedings/10.1145/3025453 (Proceedings)


ConferenceInternational Conference on Human Factors in Computing Systems 2017
Abbreviated titleCHI 2017
Country/TerritoryUnited States of America
Internet address


  • Event sequence data
  • Eye tracking
  • Matrices
  • Network visualisation
  • Node-link diagrams
  • Sankey diagrams

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