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Visual Encodings for Networks with Multiple Edge Types

Vogogias, T.; Archambault, D. W.; Bach, B.; Kennedy, Jessie


T. Vogogias

D. W. Archambault

B. Bach


This paper reports on a formal user study on visual encodings of networks with multiple edge types in adjacency matrices. Our tasks and conditions were inspired by real problems in computational biology. We focus on encodings in adjacency matrices, selecting four designs from a potentially huge design space of visual encodings. We then settle on three visual variables to evaluate in a crowdsourcing study with 159 participants: orientation, position and colour. The best encodings were integrated into a visual analytics tool for inferring dynamic Bayesian networks and evaluated by computational biologists for additional evidence. We found that the encodings performed differently depending on the task, however, colour was found to help in all tasks except when trying to find the edge with the largest number of edge types. Orientation generally outperformed position in all of our tasks.

Presentation Conference Type Conference Paper (Published)
Conference Name International Conference on Advanced Visual Interfaces
Start Date Oct 28, 2020
End Date Nov 2, 2020
Acceptance Date Mar 29, 2020
Publication Date 2020-09
Deposit Date Apr 14, 2020
Publicly Available Date Sep 30, 2020
Publisher Association for Computing Machinery (ACM)
Book Title AVI '20: Proceedings of the International Conference on Advanced Visual Interfaces
Keywords information visualization
Public URL


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