Controllable and progressive edge clustering for large networks

Authors:
Huamin Qu;Hong Zhou;Yingcai Wu
Affiliations:
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
Venue:
GD'06 Proceedings of the 14th international conference on Graph drawing
Year:
2006

Citing 6
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Abstract

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.