Abstract
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 language | English |
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Title of host publication | Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems |
Editors | Cliff Lampe, m.c. schraefel |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1397-1407 |
Number of pages | 11 |
ISBN (Print) | 9781450346559 |
DOIs | |
Publication status | Published - 2017 |
Event | International Conference on Human Factors in Computing Systems 2017 - Colorado Convention Center, Denver, United States of America Duration: 6 May 2017 → 11 May 2017 Conference number: 35th https://chi2017.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3025453 (Proceedings) |
Conference
Conference | International Conference on Human Factors in Computing Systems 2017 |
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Abbreviated title | CHI 2017 |
Country | United States of America |
City | Denver |
Period | 6/05/17 → 11/05/17 |
Internet address |
Keywords
- Event sequence data
- Eye tracking
- Matrices
- Network visualisation
- Node-link diagrams
- Sankey diagrams