Browsing by Author "Archambault, Daniel"

Now showing 1 - 9 of 9
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    EuroVis 2023 CGF 42-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Bujack, Roxana; Archambault, Daniel; Schreck, Tobias; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
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    Faster Edge‐Path Bundling through Graph Spanners
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Wallinger, Markus; Archambault, Daniel; Auber, David; Nöllenburg, Martin; Peltonen, Jaakko; Hauser, Helwig and Alliez, Pierre
    Edge‐Path bundling is a recent edge bundling approach that does not incur ambiguities caused by bundling disconnected edges together. Although the approach produces less ambiguous bundlings, it suffers from high computational cost. In this paper, we present a new Edge‐Path bundling approach that increases the computational speed of the algorithm without reducing the quality of the bundling. First, we demonstrate that biconnected components can be processed separately in an Edge‐Path bundling of a graph without changing the result. Then, we present a new edge bundling algorithm that is based on observing and exploiting a strong relationship between Edge‐Path bundling and graph spanners. Although the worst case complexity of the approach is the same as of the original Edge‐Path bundling algorithm, we conduct experiments to demonstrate that the new approach is – times faster than Edge‐Path bundling depending on the dataset, which brings its practical running time more in line with traditional edge bundling algorithms.
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    MLVis 2019: Frontmatter
    (The Eurographics Association, 2019) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
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    MLVis 2020: Frontmatter
    (The Eurographics Association, 2020) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
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    MLVis 2021: Frontmatter
    (The Eurographics Association, 2021) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
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    MLVis 2022: Frontmatter
    (The Eurographics Association, 2022) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
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    MLVis 2023: Frontmatter
    (The Eurographics Association, 2023) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
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    A Multilevel Approach for Event-Based Dynamic Graph Drawing
    (The Eurographics Association, 2021) Arleo, Alessio; Miksch, Silvia; Archambault, Daniel; Agus, Marco and Garth, Christoph and Kerren, Andreas
    The timeslice is the predominant method for drawing and visualizing dynamic graphs. However, when nodes and edges have real coordinates along the time axis, it becomes difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event-based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to have its own real-valued time coordinate. Nodes are represented as trajectories of adaptive complexity that are drawn directly in the three-dimensional space-time cube (2D + t). Existing work has demonstrated clear advantages for this approach, but these advantages come at a running time cost. In response to this scalability issue, we present MultiDynNoS, the first multilevel approach for event-based dynamic graph drawing. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP, and FM3, which we couple with an event-based graph drawing algorithm. We evaluate our approach on a selection of real graphs, showing that it outperforms timeslice-based and existing event-based techniques.
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    Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sondag, Max; Turkay, Cagatay; Xu, Kai; Matthews, Louise; Mohr, Sibylle; Archambault, Daniel; ; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex 'infection maps' of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.