Visualizing Graphs as Maps with Contiguous Regions

Relational datasets, which include clustering information, can be visualized with tools such as BubbleSets, LineSets, SOM, and GMap. The countries in SOM-based and GMap-based visualizations are fragmented, i.e., they are represented by several disconnected regions. While BubbleSets and LineSets have contiguous regions, these regions may overlap, even when the input clustering is non-overlapping. We describe two methods for creating non-fragmented and non-overlapping maps within the GMap framework. The first approach achieves contiguity by preserving the given embedding and creating a clustering based on geometric proximity. The second approach achieves contiguity by preserving the clustering information. The methods are quantitatively evaluated using embedding and clustering metrics, and their usefulness is demonstrated with several real-world datasets and a fullyfunctional online system at gmap.cs.arizona.edu.

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

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

[3]  Anne Verroust-Blondet,et al.  Ensuring the Drawability of Extended Euler Diagrams for up to 8 Sets , 2004, Diagrams.

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

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

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

[7]  Catherine A. Sugar,et al.  Finding the Number of Clusters in a Dataset , 2003 .

[8]  Sara Irina Fabrikant,et al.  Spatialization Methods: A Cartographic Research Agenda for Non-geographic Information Visualization , 2003 .

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

[10]  Peter J. Stuckey,et al.  Fast Node Overlap Removal , 2005, GD.

[11]  Ulrik Brandes,et al.  Experiments on Graph Clustering Algorithms , 2003, ESA.

[12]  Yifan Hu,et al.  How to Display Group Information on Node-Link Diagrams: An Evaluation , 2014, IEEE Transactions on Visualization and Computer Graphics.

[13]  Yifan Hu,et al.  A Maxent-Stress Model for Graph Layout , 2012, IEEE Transactions on Visualization and Computer Graphics.

[14]  Mary Czerwinski,et al.  Design Study of LineSets, a Novel Set Visualization Technique , 2011, IEEE Transactions on Visualization and Computer Graphics.

[15]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[16]  Daniel Fried,et al.  Maps of Computer Science , 2013, 2014 IEEE Pacific Visualization Symposium.

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

[18]  André Skupin,et al.  A cartographic approach to visualizing conference abstracts , 2002 .

[19]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[20]  Andrew Fish,et al.  General Euler Diagram Generation , 2008, Diagrams.

[21]  Charu C. Aggarwal,et al.  Graph Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

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

[23]  Yifan Hu,et al.  A maxent-stress model for graph layout , 2012, PacificVis.

[24]  Katy Börner,et al.  Plug-and-play macroscopes , 2011, Commun. ACM.

[25]  Niklas Elmqvist,et al.  Improving revisitation in graphs through static spatial features , 2011, Graphics Interface.

[26]  Daniel W. Archambault,et al.  ImPrEd: An Improved Force‐Directed Algorithm that Prevents Nodes from Crossing Edges , 2011, Comput. Graph. Forum.

[27]  Jarkko Venna,et al.  Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization , 2010, J. Mach. Learn. Res..