Visual Encodings for Networks with Multiple Edge Types

Vogogias, Athanasios; Archambault, Daniel; Bach, Benjamin; Kennedy, Jessie

doi:10.1145/3399715.3399827

Conference Paper/Proceeding/Abstract 664 views 255 downloads

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

PDF | Accepted Manuscript

Conference was in June, but moved to October. It is okay to publish to cronfa by june.
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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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first_indexed	2020-04-19T19:40:25Z
last_indexed	2020-10-06T03:17:11Z
id	cronfa53990
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spelling	2020-10-05T16:50:41.7325955 v2 53990 2020-04-19 Visual Encodings for Networks with Multiple Edge Types 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2020-04-19 SCS 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. Conference Paper/Proceeding/Abstract Proceedings of the International Conference on Advanced Visual Interfaces 1 9 ACM New York, NY, USA 9781450375351 2 10 2020 2020-10-02 10.1145/3399715.3399827 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-10-05T16:50:41.7325955 2020-04-19T13:30:41.5055892 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Athanasios Vogogias 1 Daniel Archambault 0000-0003-4978-8479 2 Benjamin Bach 3 Jessie Kennedy 4 53990__17096__a9aeec0b3ccf4ed38af117651a48f62d.pdf AVI2020_Visual_Encodings_for_Networks_with_Multiple_Edge_Types___REVISED.pdf 2020-04-19T13:34:52.3806065 Output 1322881 application/pdf Accepted Manuscript true 2020-06-01T00:00:00.0000000 Conference was in June, but moved to October. It is okay to publish to cronfa by june. false eng
title	Visual Encodings for Networks with Multiple Edge Types
spellingShingle	Visual Encodings for Networks with Multiple Edge Types Daniel Archambault
title_short	Visual Encodings for Networks with Multiple Edge Types
title_full	Visual Encodings for Networks with Multiple Edge Types
title_fullStr	Visual Encodings for Networks with Multiple Edge Types
title_full_unstemmed	Visual Encodings for Networks with Multiple Edge Types
title_sort	Visual Encodings for Networks with Multiple Edge Types
author_id_str_mv	8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv	8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author	Daniel Archambault
author2	Athanasios Vogogias Daniel Archambault Benjamin Bach Jessie Kennedy
format	Conference Paper/Proceeding/Abstract
container_title	Proceedings of the International Conference on Advanced Visual Interfaces
container_start_page	1
publishDate	2020
institution	Swansea University
isbn	9781450375351
doi_str_mv	10.1145/3399715.3399827
publisher	ACM
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description	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.
published_date	2020-10-02T04:07:16Z
_version_	1763753523835568128
score	11.036116

Visual Encodings for Networks with Multiple Edge Types

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