A Framework for Visualising Large Graphs

Visualising large graphs faces the challenges of both data complexity and visual complexity. This paper presents a framework for visualising large graphs that reduces data complexity using the clustered graph model and provides users with navigational approaches for browsing clustered graphs. A key design task of such a system is to define a for generating logical abstractions of a clustered graph during navigation. An appropriate abstraction strategy should represent a clustered graph well and avoid visual overload. The semantic fisheye view of a clustered graph is proposed for such a purpose. Two case studies were investigated, and the experiment results show that during navigation the first-order fisheye view of a clustered graph conserves visual complexity at a constant level.

• Type: Conference
• Year: 2005
• Where: IV
• Authors: Wanchun Li, Seok-Hee Hong, Peter Eades
• Keywords: Data Complexity, Information Technology, IV 2005, Large Graphs, Visual Complexity