GRAVITE Research team

Graph Visualization and Interactive Exploration

Team presentation

GRAVITE aims at designing interactive visualization methods and tools to analyze and mine large datasets. Our emphasis is on the visualization of graph structures to help users gain insights from large datasets and large-scale simulations, to understand the data and/or the underlying model, and ultimately, to identify intrinsic properties or emergent phenomenon. More than just being able to deal with large volume and inhomogeneous data, we are required to deal with constant changes in data, possibly making it ambiguous and uncertain. In the context of graph visualization, the challenge we face is thus to design methods and tools:
  • to deal with large and dynamically changing graphs;
  • to visually identify salient properties in changing substructures;
  • identify the multiscale nature of data;
  • to produce visual cues helping the user to track such changes in situations where dynamic graphs occur.
Our collaborations with experts of other scientific fields as well as with industry contribute to the overall organization of this research agenda and serve a twofold objective:
  • to build theoretical knowledge relevant to information visualization and visual analytics, and develop a sound methodology for graph visualization and navigation;
  • to target transfer opportunities favoring the adoption of our ideas and technology by other scientific communities and by the industry.

Research themes

The core strength of our team resides in the development of combinatorial mathematics and graph algorithmics to serve the aims of graph visualization. We deploy our mathematical and algorithmic skills in Information Visualization to develop:
  • Graph statistics: that capture key properties of the data, including scalable implementations;
  • Clustering methods: that handle large datasets both visually and computationally;
  • Graph hierarchies: that transform large graphs into a hierarchy of smaller, more readable and easier-to-manipulate sub-structures;
  • Graph drawing algorithms: that lay out large datasets rapidly, enhancing scalability and addressing domain-specific conventions and requirements;
  • Interactions: that exploit graph hierarchies as a central mechanism for navigating large graphs, while taking domain-specific tasks into account;
  • Evaluation methods: that generate artificial datasets (randomly) based on key properties of the target data.

Keywords: Information Visualization Visual Analytics Graph Mining Interactive Exploration

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