Affiliation
Univ ArizonaIssue Date
2020-07Keywords
Heuristic algorithmsData visualization
Dynamics
Three-dimensional displays
Computational modeling
Layout
Animation
Information visualisation
graph drawing
dynamic graphs
event-based analytics
no timeslices
Metadata
Show full item recordPublisher
IEEE COMPUTER SOCCitation
Simonetto, P., Archambault, D., & Kobourov, S. (2020). Event-Based Dynamic Graph Visualisation. IEEE transactions on visualization and computer graphics, 26(7), 2373–2386. https://doi.org/10.1109/TVCG.2018.2886901Rights
Copyright © The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed algorithms can exploit to compute a layout. However, often dynamic graphs are expressed as a series of events where the nodes and edges have real coordinates along the time dimension that are not confined to discrete timeslices. Current techniques for dynamic graph drawing impose a set of timeslices on this event-based data in order to draw the dynamic graph, but it is unclear how many timeslices should be selected: too many timeslices slows the computation of the layout, while too few timeslices obscures important temporal features, such as causality. To address these limitations, we introduce a novel model for drawing event-based dynamic graphs and the first dynamic graph drawing algorithm, DynNoSlice, that is capable of drawing dynamic graphs in this model. DynNoSlice is an offline, force-directed algorithm that draws event-based, dynamic graphs in the space-time cube (2D+time). We also present a method to extract representative small multiples from the space-time cube. To demonstrate the advantages of our approach, DynNoSlice is compared with state-of-the-art timeslicing methods using a metrics-based experiment. Finally, we present case studies of event-based dynamic data visualised with the new model and algorithm.Note
Open access articleISSN
1077-2626EISSN
1941-0506PubMed ID
30575538Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1109/TVCG.2018.2886901
Scopus Count
Collections
Except where otherwise noted, this item's license is described as Copyright © The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
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