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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 strategy 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.