Embedding, clustering and coloring for dynamic maps

We describe a practical approach for visualizing multiple relationships defined on the same dataset using a geographic map metaphor, where clusters of nodes form countries and neighboring countries correspond to nearby clusters. Our aim is to provide a visualization that allows us to compare two or more such maps (showing an evolving dynamic process, or obtained using different relationships). In the case where we are considering multiple relationships, e.g., different similarity metrics, we also provide an interactive tool to visually explore the effect of combining two or more such relationships. Our method ensures good readability and mental map preservation, based on dynamic node placement with node stability, dynamic clustering with cluster stability, and dynamic coloring with color stability.

[1]  David M. Mark,et al.  The distance-similarity metaphor in region-display spatializations , 2006, IEEE Computer Graphics and Applications.

[2]  Jarke J. van Wijk,et al.  Visual Comparison of Hierarchically Organized Data , 2008, Comput. Graph. Forum.

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

[4]  Amit R. Indap,et al.  Genes mirror geography within Europe , 2008, Nature.

[5]  Yifan Hu,et al.  Visualizing Graphs and Clusters as Maps , 2010, IEEE Computer Graphics and Applications.

[6]  Ivan Herman,et al.  Graph Visualization and Navigation in Information Visualization: A Survey , 2000, IEEE Trans. Vis. Comput. Graph..

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

[8]  Furu Wei,et al.  Context preserving dynamic word cloud visualization , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[9]  Ulrik Brandes,et al.  On Finding Graph Clusterings with Maximum Modularity , 2007, WG.

[10]  Deepayan Chakrabarti,et al.  Evolutionary clustering , 2006, KDD '06.

[11]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[12]  Giuseppe Di Battista,et al.  Topographic Visualization of Prefix Propagation in the Internet , 2006, IEEE Transactions on Visualization and Computer Graphics.

[13]  Ulrik Brandes,et al.  A Bayesian Paradigm for Dynamic Graph Layout , 1997, GD.

[14]  Robert E. Tarjan,et al.  Faster Scaling Algorithms for Network Problems , 1989, SIAM J. Comput..

[15]  Ulrik Brandes,et al.  A Quantitative Comparison of Stress-Minimization Approaches for Offline Dynamic Graph Drawing , 2011, GD.

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

[17]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Ulrik Brandes,et al.  Visual unrolling of network evolution and the analysis of dynamic discourse , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[19]  Hongzhe Li,et al.  Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine , 2005, Human Genetics.

[20]  Yifan Hu,et al.  Visualizing dynamic data with maps , 2011, 2011 IEEE Pacific Visualization Symposium.

[21]  Kozo Sugiyama,et al.  Layout Adjustment and the Mental Map , 1995, J. Vis. Lang. Comput..

[22]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

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

[24]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Derek Greene,et al.  Spectral Co-Clustering for Dynamic Bipartite Graphs , 2010, NyNaK.

[26]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[27]  Emden R. Gansner,et al.  Graphviz and Dynagraph – Static and Dynamic Graph Drawing Tools , 2003 .

[28]  Dorothea Wagner,et al.  Modularity-Driven Clustering of Dynamic Graphs , 2010, SEA.

[29]  Stephan Diehl,et al.  Graphs, They Are Changing , 2002, GD.

[30]  Robert E. Tarjan,et al.  Fibonacci heaps and their uses in improved network optimization algorithms , 1984, JACM.

[31]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[32]  Michael Burch,et al.  TimeRadarTrees: Visualizing Dynamic Compound Digraphs , 2008, Comput. Graph. Forum.

[33]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[34]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[35]  Xiaoji Chen,et al.  The Connected States of America: Quantifying Social Radii of Influence , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

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