GMap: Visualizing graphs and clusters as maps

Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap, a practical algorithm for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains.

[1]  Yehuda Koren,et al.  Graph Drawing by Stress Majorization , 2004, GD.

[2]  Andreas Noack,et al.  Modularity clustering is force-directed layout , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  David Eppstein,et al.  Choosing Colors for Geometric Graphs via Color Space Embeddings , 2006, GD.

[4]  Jennifer A. Scott,et al.  A Multilevel Algorithm for Wavefront Reduction , 2001, SIAM J. Sci. Comput..

[5]  W. Marsden.  I and J , 2012 .

[6]  Alexandru Telea,et al.  Visualization of areas of interest in software architecture diagrams , 2006, SoftVis '06.

[7]  Timothy A. Davis,et al.  The university of Florida sparse matrix collection , 2011, TOMS.

[8]  Rongchun Zhao,et al.  A fractal-based relaxation algorithm for shape from terrain image , 2008, Comput. Vis. Image Underst..

[9]  Daniel W. Archambault,et al.  Fully Automatic Visualisation of Overlapping Sets , 2009, Comput. Graph. Forum.

[10]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[11]  Steven Fortune,et al.  A sweepline algorithm for Voronoi diagrams , 1986, SCG '86.

[12]  Jarke J. van Wijk,et al.  Squarified Treemaps , 2000, VisSym.

[13]  Patrice Ossona de Mendez,et al.  On Triangle Contact Graphs , 1994, Combinatorics, Probability and Computing.

[14]  Michael Kaufmann,et al.  Optimal Polygonal Representation of Planar Graphs , 2011, Algorithmica.

[15]  Xin He,et al.  On Floor-Plan of Plane Graphs , 1999, SIAM J. Comput..

[16]  Yifan Hu,et al.  Efficient, High-Quality Force-Directed Graph Drawing , 2006 .

[17]  Yifan Hu,et al.  Putting recommendations on the map: visualizing clusters and relations , 2009, RecSys '09.

[18]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[19]  M. Sheelagh T. Carpendale,et al.  Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations , 2009, IEEE Transactions on Visualization and Computer Graphics.

[20]  Ben Shneiderman,et al.  Tree visualization with tree-maps: 2-d space-filling approach , 1992, TOGS.

[21]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Stephen G. Kobourov,et al.  GraphAEL: Graph Animations with Evolving Layouts , 2003, GD.

[23]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[24]  F. K. Musgrave.  Methods for realistic landscape imaging , 1993 .

[25]  Kevin W. Boyack,et al.  Mapping the backbone of science , 2004, Scientometrics.

[26]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[27]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[28]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[29]  A. Pothen,et al.  Two improved algorithms for envelope and wavefront reduction , 1997 .

[30]  E. Raisz.  The Rectangular Statistical Cartogram , 1934 .

[31]  Hsueh-I Lu,et al.  Compact floor-planning via orderly spanning trees , 2003, J. Algorithms.