Visualizing Graphs and Clusters as Maps
Emden R. Gansner, Yifan Hu, Stephen Kobourov · IEEE Computer Graphics and Applications · 2010
Information visualization is essential in making sense of large datasets. Often, high-dimensional data are visualized as a collection of points in 2D space through dimensionality reduction techniques. However, these traditional methods often don't capture the underlying structural information, clustering, and neighborhoods well. GMap is a practical algorithmic framework for visualizing relational data with geographic-like maps. This approach is effective in various domains.