Literature DB >> 30575538

Event-Based Dynamic Graph Visualisation.

Paolo Simonetto, Daniel Archambault, Stephen Kobourov.   

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.

Year:  2018        PMID: 30575538     DOI: 10.1109/TVCG.2018.2886901

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  2 in total

1.  RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses.

Authors:  M Chen; A Abdul-Rahman; D Archambault; J Dykes; P D Ritsos; A Slingsby; T Torsney-Weir; C Turkay; B Bach; R Borgo; A Brett; H Fang; R Jianu; S Khan; R S Laramee; L Matthews; P H Nguyen; R Reeve; J C Roberts; F P Vidal; Q Wang; J Wood; K Xu
Journal:  Epidemics       Date:  2022-04-28       Impact factor: 5.324

2.  Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

Authors:  Jason Dykes; Alfie Abdul-Rahman; Daniel Archambault; Benjamin Bach; Rita Borgo; Min Chen; Jessica Enright; Hui Fang; Elif E Firat; Euan Freeman; Tuna Gönen; Claire Harris; Radu Jianu; Nigel W John; Saiful Khan; Andrew Lahiff; Robert S Laramee; Louise Matthews; Sibylle Mohr; Phong H Nguyen; Alma A M Rahat; Richard Reeve; Panagiotis D Ritsos; Jonathan C Roberts; Aidan Slingsby; Ben Swallow; Thomas Torsney-Weir; Cagatay Turkay; Robert Turner; Franck P Vidal; Qiru Wang; Jo Wood; Kai Xu
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2022-08-15       Impact factor: 4.019

  2 in total

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