Visual Analysis of Large Heterogeneous Network through Interactive Centrality Based Sampling

The visualization of large networks is a challenging problem due to the limitation of human visual perception. Sampling technology is an essential strategy in most of large network visualization and analysis. Moreover, when we consider large heterogeneous networks consisting various types of nodes and links, which has attracted much attention from multiple disciplines, it is much more difficult for users to explore and mine some useful information in such complex networks. Thus to design sampling methods for large heterogeneous network visualization has become a challenge. However, most of existing sampling methods ignore the types of nodes and links, hence they could not be used for heterogeneous network directly. In this paper, we propose an eigenvector centrality based sampling method considering both multiple types of nodes and links, supporting sampling the heterogeneous networks directly and efficiently. Furthermore, we develop HeteVis, a prototype visualization system with the proposed sampling algorithm built-in. The parameters of HeteVis can be dynamically personalized through user interactions and such system can help users to explore real large heterogeneous networks easily and flexibly. Extensive case study using the UCI KDD Movie network demonstrates how users can apply HeteVis to explore the heterogeneous networks.