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Visual Encodings for Networks with Multiple Edge Types / Athanasios Vogogias; Daniel Archambault; Benjamin Bach; Jessie Kennedy

Proceedings of the International Conference on Advanced Visual Interfaces, Pages: 1 - 9

Swansea University Author: Daniel, Archambault

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DOI (Published version): 10.1145/3399715.3399827

Abstract

This paper reports on a formal user study on visual encodings ofnetworks with multiple edge types in adjacency matrices. Our tasksand conditions were inspired by real problems in computationalbiology. We focus on encodings in adjacency matrices, selectingfour designs from a potentially huge design s...

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Published in: Proceedings of the International Conference on Advanced Visual Interfaces
ISBN: 9781450375351
Published: New York, NY, USA ACM 2020
URI: https://cronfa.swan.ac.uk/Record/cronfa53990
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Abstract: This paper reports on a formal user study on visual encodings ofnetworks with multiple edge types in adjacency matrices. Our tasksand conditions were inspired by real problems in computationalbiology. We focus on encodings in adjacency matrices, selectingfour designs from a potentially huge design space of visual encodings.We then settle on three visual variables to evaluate in acrowdsourcing study with 159 participants: orientation, positionand colour. The best encodings were integrated into a visual analyticstool for inferring dynamic Bayesian networks and evaluated bycomputational biologists for additional evidence.We found that theencodings performed differently depending on the task, however,colour was found to help in all tasks except when trying to find theedge with the largest number of edge types. Orientation generallyoutperformed position in all of our tasks.
College: College of Science
Start Page: 1
End Page: 9