25 citations · 44 across the 6 of their papers we have counts for
6 papers
Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach
Markus Wallinger, Daniel Archambault, David Auber +2
Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to d…
The Turing Test for Graph Drawing Algorithms
Helen C. Purchase, Daniel Archambault, Stephen Kobourov +3
Do algorithms for drawing graphs pass the Turing Test? That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred ex…
Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation
Vahan Yoghourdjian, Daniel Archambault, Stephan Diehl +4
For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experim…
Drawing Dynamic Graphs Without Timeslices
Paolo Simonetto, Daniel Archambault, Stephen Kobourov
Timeslices are often used to draw and visualize dynamic graphs. While timeslices are a natural way to think about dynamic graphs, they are routinely imposed on continuous data. Oft…
TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data
Derek Greene, Daniel Archambault, Václav Belák +1
For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular t…
TwitterCrowds: Techniques for Exploring Topic and Sentiment in Microblogging Data
Daniel Archambault, Derek Greene, Pádraig Cunningham
Analysts and social scientists in the humanities and industry require techniques to help visualize large quantities of microblogging data. Methods for the automated analysis of lar…