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 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.
Original language | English |
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Title of host publication | Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI'19) |
Place of Publication | New York |
Publisher | ACM |
Number of pages | 12 |
ISBN (Electronic) | 9781450359702 |
DOIs | |
Publication status | Published - 18 Apr 2019 |
Event | ACM Conference on Human Factors in Computing Systems 2019 - SEC, Glasgow, United Kingdom Duration: 4 May 2019 → 9 May 2019 http://chi2019.acm.org/ |
Conference
Conference | ACM Conference on Human Factors in Computing Systems 2019 |
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Abbreviated title | CHI 2019 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 4/05/19 → 9/05/19 |
Internet address |
Keywords
- Information visualization
- Dynamic network analysis
- Large display visualization