In temporal (or event-based) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a 2D + t space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems ranging from sub-par performance on some tasks to loss of precision. In this paper, we present TimeLighting, a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights the node trajectories and their mobility over time, visualizes...
International audienceGraph embedding aims to learn a representation of graphs' nodes in a latent lo...
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, ...
The temporal dimension increases the complexity of network models but also provides more detailed in...
International audienceWe review a range of temporal data visualization techniques through a new lens...
We review a range of temporal data visualization techniques through a new lens, by describing them a...
Dynamic networks can be challenging to analyze visually, especially if they span a large time range ...
International audienceWe present GraphDiaries, an interface for temporal navigation in networks chan...
Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed ...
International audienceWe present the generalized space-time cube, a descriptive model for visualizat...
International audienceIdentifying, tracking and understanding changes in dynamic networks are comple...
A \emph{temporal graph} is, informally speaking, a graph that changes with time. When time is discre...
Various time-based visualization techniques have been designed to support the temporal analysis of d...
In this paper, we present a new approach to exploring dynamic graphs. We have developed a new cluste...
(MacEachren 1995, pp.252, 254). Here time is treated as the third (vertical) spatial dimension while...
Temporal networks are increasingly being used to model the interactions of complex systems. Most stu...
International audienceGraph embedding aims to learn a representation of graphs' nodes in a latent lo...
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, ...
The temporal dimension increases the complexity of network models but also provides more detailed in...
International audienceWe review a range of temporal data visualization techniques through a new lens...
We review a range of temporal data visualization techniques through a new lens, by describing them a...
Dynamic networks can be challenging to analyze visually, especially if they span a large time range ...
International audienceWe present GraphDiaries, an interface for temporal navigation in networks chan...
Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed ...
International audienceWe present the generalized space-time cube, a descriptive model for visualizat...
International audienceIdentifying, tracking and understanding changes in dynamic networks are comple...
A \emph{temporal graph} is, informally speaking, a graph that changes with time. When time is discre...
Various time-based visualization techniques have been designed to support the temporal analysis of d...
In this paper, we present a new approach to exploring dynamic graphs. We have developed a new cluste...
(MacEachren 1995, pp.252, 254). Here time is treated as the third (vertical) spatial dimension while...
Temporal networks are increasingly being used to model the interactions of complex systems. Most stu...
International audienceGraph embedding aims to learn a representation of graphs' nodes in a latent lo...
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, ...
The temporal dimension increases the complexity of network models but also provides more detailed in...