A Space Efficient Clustered Visualization of Large Graphs

Abstract

This paper proposes a new technique for visualizing large graphs of several ten thousands of vertices and edges. To achieve the graph abstraction, a hierarchical clustered graph is extracted from a general large graph based on the community structures which are discovered in the graph. An enclosure geometrical partitioning algorithm is then applied to achieve the space optimization. For graph drawing, we technically use the combination of a spring-embbeder algorithm and circular drawings that archives the goal of optimization of display space and aesthetical niceness. We also discuss an associated interaction mechanism accompanied with the layout solution. Our interaction not only allows users to navigate hierarchically up and down through the entire clustered graph, but also provides a way to navigate multiple clusters concurrently. Animation is also implemented to preserve users' mental maps during the interaction.