Daniel Archambault

Newcastle University

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Guy Mélançon

Laboratoire Bordelais de Recherche en Informatique

Bruno Pinaud

Laboratoire Bordelais de Recherche en Informatique

Helen C. Purchase

Monash University

David Auber

Université de Bordeaux

Tatiana von Landesberger

University of Cologne

Benjamin Renoust

National Institute of Informatics

Andreas Kerren

Linnaeus University

Mohammad Ghoniem

Luxembourg Institute of Science and Technology

Benoît Otjacques

Luxembourg Institute of Science and Technology

Fintan McGee

Luxembourg Institute of Science and Technology

Cooperative Institutions

Google (United States)

Institut national de recherche en informatique et en automatique

Swansea University

Centre National de la Recherche Scientifique

Laboratoire Bordelais de Recherche en Informatique

TU Wien

Université de Bordeaux

University of Utah

Los Alamos National Laboratory

University of Oxford

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Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Lecture notes in computer science (2017)

Daniel Archambault Helen C. Purchase Tobias Hoßfeld

Crowdsourcing

10.1007/978-3-319-66435-4

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Citations (21)

TopoLayout: Multilevel Graph Layout by Topological Features

IEEE Transactions on Visualization and Computer Graphics (2007)

Daniel Archambault Tamara Munzner David Auber

We describe TopoLayout, a feature-based, multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is drawn with an algorithm tuned for its topology. As would be expected from a feature-based approach, the runtime and visual quality of TopoLayout depends on the number and types of topological features present in the graph. We show experimental results comparing speed and visual quality for TopoLayout against four other multilevel algorithms on a variety of data sets with a range of connectivities and sizes. TopoLayout frequently improves the results in terms of speed and visual quality on these data sets

Feature (linguistics)

Graph Layout

Topological graph theory

10.1109/tvcg.2007.46

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Citations (146)

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

Epidemics (2022)

Min Chen Alfie Abdul‐Rahman Daniel Archambault Jason Dykes Panagiotis D. Ritsos

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.

Contact tracing

10.1016/j.epidem.2022.100569

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New Results - Graph statistics and graph clustering

Daniel Archambault Romain Bourqui Maylis Delest Frédéric Gilbert Guy Mélançon

Clustering coefficient

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Conclusions

Synthesis lectures on visualization (2021)

Fintan McGee Mohammad Ghoniem Benoît Otjacques Benjamin Renoust Daniel Archambault

Throughout this book we have explored the many different perspectives that are required for the successful visual analysis of multilayer networks. We have shown that multilayer network problems are at the intersection of domain and data, and that a multilayer network approach to visualization and analysis can be applied to many domains. We have discussed in detail how the current state of the art techniques for visualization, interaction, and analysis can be applied to the multilayer case (even when attributes are associated), covering both those that are conceived for the multilayer case, and interesting techniques that may be adapted. We have examined the importance of the layer being considered as an entity in and of itself, and have identified categories of tasks related to layers that are not covered by existing network task taxonomies. We have also discussed the many issues and challenges when it comes to evaluating multilayer visualization tools and techniques, an important topic as many existing evaluation approaches may not be suitable.

Creative visualization

10.1007/978-3-031-02608-9_9

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EulerMerge: Simplifying Euler Diagrams Through Set Merges

arXiv (Cornell University) (2023)

Xinyuan Yan Peter Rodgers Peter Rottmann Daniel Archambault Jan‐Henrik Haunert

Euler diagrams are an intuitive and popular method to visualize set-based data. In a Euler diagram, each set is represented as a closed curve, and set intersections are shown by curve overlaps. However, Euler diagrams are not visually scalable and automatic layout techniques struggle to display real-world data sets in a comprehensible way. Prior state-of-the-art approaches can embed Euler diagrams by splitting a closed curve into multiple curves so that a set is represented by multiple disconnected enclosed areas. In addition, these methods typically result in multiple curve segments being drawn concurrently. Both of these features significantly impede understanding. In this paper, we present a new and scalable method for embedding Euler diagrams using set merges. Our approach simplifies the underlying data to ensure that each set is represented by a single, connected enclosed area and that the diagram is drawn without curve concurrency, leading to well formed and understandable Euler diagrams.

10.48550/arxiv.2311.15396

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All the distant horizon edges of a terrain

Daniel Archambault

10.14288/1.0051704

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Computing the set of all distant horizons of a terrain.

Canadian Conference on Computational Geometry (2004)

William Evans Daniel Archambault David Kirkpatrick

New horizons

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Citations (2)

New Results - User Evaluation

Daniel Archambault Bruno Pinaud

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ThemeCrowds

Daniel Archambault Derek Greene Pádraig Cunningham Neil Hurley

Users of social media sites, such as Twitter, rapidly generate large volumes of text content on a daily basis. Visual summaries are needed to understand what groups of people are saying collectively in this unstructured text data. Users will typically discuss a wide variety of topics, where the number of authors talking about a specific topic can quickly grow or diminish over time, and what the collective is saying about the subject can shift as a situation develops. In this paper, we present a technique that summarises what collections of Twitter users are saying about certain topics over time. As the correct resolution for inspecting the data is unknown in advance, the users are clustered hierarchically over a fixed time interval based on the similarity of their posts. The visualisation technique takes this data structure as its input. Given a topic, it finds the correct resolution of users at each time interval and provides tags to summarise what the collective is discussing. The technique is tested on a large microblogging corpus, consisting of millions of tweets and over a million users.

Microblogging

Similarity (geometry)