Interactive Visual Summary of Major Communities in a Large Network
Yanhong Wu, Wenbin Wu, Sixiao Yang, Youliang Yan, Huamin Qu
The layout generation process: (a) Use MDS to gather strong connected cluster nodes together. (b) Original Voronoi treemap represents different clusters with the size attribute. (c) Shrink each Voronoi cell to form cluster polygons and cluster gaps. (d) Arrange external nodes along cluster gaps and adapt the corner-cutting algorithm for each cluster polygon.
In this paper, we introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a net- work using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.
VisNexus is a series of visualization projects aiming to improve our understanding of the complex relations among data entities, which is conducted by the VisLab at Hong Kong University of Science and Technology. The HKUST VisLab is a multidisciplinary group aiming at improving the visual analysis and various visualizations.
The projects are categorized into following five topics: