Visualization of large citation networks with space-efficient multi-layer optimization

This paper describes a technique for visualizing large citation networks (or bibliography networks) using a space-efficient multi-layer optimization visualization, technique. Our technique first use a fast clustering algorithm to discover community structure in the bibliographic networks. The clustering process partitions an entire network into relevant abstract subgroups so that the visualization, can provide a clearer and less density of display of global view of the complete graph of citations. We next use a new space-efficient visualization algorithm to archive the optimization of graph layout within the limited display space so that our technique can theoretically handle a very large bibliography network with several thousands of elements. Our technique also employs rich graphics to enhance the attributed property of the visualization including publication years and number of citations. Finally, the system provides an interaction technique in cooperating with the layout to allow users to navigate through the citation network. Animation is also implemented to preserve the users' mental maps during the interaction.