Visual Encodings for Networks with Multiple Edge Types

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

Abstract

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.

Citation

Vogogias, T., Archambault, D. W., Bach, B., & Kennedy, J. (2020). Visual Encodings for Networks with Multiple Edge Types. In AVI '20: Proceedings of the International Conference on Advanced Visual Interfaces. https://doi.org/10.1145/3399715.3399827

• Conference Name: International Conference on Advanced Visual Interfaces
• Conference Location: Napkes, Italy
• 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
• DOI: https://doi.org/10.1145/3399715.3399827
• Keywords: information visualization
• Public URL: http://researchrepository.napier.ac.uk/Output/2652365