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Michael Burch1, Kiet Bennema Ten Brinke2, Adrien Castella2

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Area of Science:

  • Computer Science
  • Data Visualization
  • Graph Theory

Background:

  • Visualizing dynamic graphs presents challenges due to complex relational and time-varying data.
  • Node-link diagrams suit sparse graphs, while adjacency matrices are better for dense graphs.

Purpose of the Study:

  • To introduce a visually and algorithmically scalable approach for dynamic graph visualization.
  • To enable interactive exploration by linking node-link and adjacency matrix views.

Main Methods:

  • Developed an approach combining interactively linked node-link and adjacency matrix visualizations.
  • Implemented a system where insights from one view influence others (layout, reordering).
  • Integrated automatic identification of groups, clusters, and outliers over time.

Main Results:

  • Demonstrated how insights from combined views can drive layout and reordering in other views.
  • Enabled detection, computation, and visualization of node and group importance.
  • Extended supported layout and reordering techniques based on graph dynamics.

Conclusions:

  • The proposed method offers a scalable and interactive solution for dynamic graph visualization.
  • The approach facilitates understanding of graph structures, clusters, and anomalies.
  • User experiments confirmed the usability and usefulness of the visualization tool for real-world datasets.