Conference Paper/Proceeding/Abstract 823 views 224 downloads
Dynamic Network Plaid
CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Pages: 1 - 14
Swansea University Author: Daniel Archambault
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DOI (Published version): 10.1145/3290605.3300360
Abstract
Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly c...
Published in: | CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems |
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ISBN: | 978-1-4503-5970-2 |
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Glasgow
CHI Glasgow 2019
2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa48160 |
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2020-08-05T15:26:39.3236192 v2 48160 2019-01-14 Dynamic Network Plaid 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2019-01-14 SCS Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram. Conference Paper/Proceeding/Abstract CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems 1 14 CHI Glasgow 2019 Glasgow 978-1-4503-5970-2 31 12 2019 2019-12-31 10.1145/3290605.3300360 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-08-05T15:26:39.3236192 2019-01-14T13:23:02.7053430 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alexandra Lee 1 Daniel Archambault 0000-0003-4978-8479 2 Miguel Nacenta 3 0048160-14012019132641.pdf Dynamic_Network_Plaid.pdf 2019-01-14T13:26:41.6030000 Output 2856556 application/pdf Accepted Manuscript true 2019-05-04T00:00:00.0000000 true eng |
title |
Dynamic Network Plaid |
spellingShingle |
Dynamic Network Plaid Daniel Archambault |
title_short |
Dynamic Network Plaid |
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Dynamic Network Plaid |
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Dynamic Network Plaid |
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Dynamic Network Plaid |
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Dynamic Network Plaid |
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8fa6987716a22304ef04d3c3d50ef266 |
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8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault |
author |
Daniel Archambault |
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Alexandra Lee Daniel Archambault Miguel Nacenta |
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Conference Paper/Proceeding/Abstract |
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CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems |
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Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram. |
published_date |
2019-12-31T03:58:27Z |
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10.993396 |