Controllable and Progressive Edge Clustering for Large Networks

  • Huamin Qu
  • Hong Zhou
  • Yingcai Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4372)


Node-link diagrams are widely used in information visualization to show relationships among data. However, when the size of data becomes very large, node-link diagrams will become cluttered and visually confusing for users. In this paper, we propose a novel controllable edge clustering method based on Delaunay triangulation to reduce visual clutter for node-link diagrams. Our method uses curves instead of straight lines to represent links and these curves can be grouped together according to their relative positions and directions. We further introduce progressive edge clustering to achieve continuous level-of-details for large networks.


Control Point Delaunay Triangulation Node Position Virtual Node Information Visualization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Huamin Qu
    • 1
  • Hong Zhou
    • 1
  • Yingcai Wu
    • 1
  1. 1.Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, KowloonHong Kong

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