The Graph Landscape: using visual analytics for graph set analysis

AbstractIn a variety of research and application areas, graphs are an important structure for data modeling and analysis. While graph properties can have a crucial influence on the performance of graph algorithms, and thus on the outcome of experiments, often only basic analysis of the graphs under investigation in an experimental evaluation is performed and a few characteristics are reported in publications. We present Graph Landscape, a concept for the visual analysis of graph set properties. The Graph Landscape aims to support researchers to explore graphs and graph sets regarding their properties, to allow to select good experimental test sets, analyze newly generated sets, compare sets and assess the validity (or range) of experimental results and corresponding conclusions.Graphical AbstractGraphical Abstract text

[1]  Karsten Klein,et al.  Algorithm Engineering: Concepts and Practice , 2010, Experimental Methods for the Analysis of Optimization Algorithms.

[2]  Vladimir Batagelj,et al.  Social Network Analysis, Large-Scale , 2009, Encyclopedia of Complexity and Systems Science.

[3]  Daniel A. Keim,et al.  Interactive feature space extension for multidimensional data projection , 2015, Neurocomputing.

[4]  Natasha M. Maurits,et al.  Tiled Parallel Coordinates for the Visualization of Time-Varying Multichannel EEG Data , 2005, EuroVis.

[5]  Nadezhda T. Doncheva,et al.  Topological analysis and interactive visualization of biological networks and protein structures , 2012, Nature Protocols.

[6]  Jeffrey Heer,et al.  GraphPrism: compact visualization of network structure , 2012, AVI.

[7]  Karsten Klein,et al.  The Open Graph Archive: A Community-Driven Effort , 2011, Graph Drawing.

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

[9]  Stephen G. Kobourov,et al.  On the Usability of Lombardi Graph Drawings , 2012, Graph Drawing.

[10]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[11]  J. Hartigan.  Printer graphics for clustering , 1975 .

[12]  Gintautas Dzemyda,et al.  Large-Scale Multidimensional Data Visualization: A Web Service for Data Mining , 2011, ServiceWave.

[13]  R. Menezes,et al.  Entropy-Based Independence Test , 2006 .

[14]  Haim Levkowitz,et al.  Projection inspector: Assessment and synthesis of multidimensional projections , 2015, Neurocomputing.

[15]  Andrew Kennedy,et al.  The Graph Landscape: a Concept for the Visual Analysis of Graph Set Properties , 2015, VINCI.

[16]  Jinjun Chen,et al.  A Space-Filling Multidimensional Visualization (SFMDVis for Exploratory Data Analysis , 2014, VINCI '14.

[17]  Soojin V Yi,et al.  Path lengths in protein–protein interaction networks and biological complexity , 2011, Proteomics.

[18]  Michael Jünger,et al.  The Open Graph Drawing Framework (OGDF) , 2013, Handbook of Graph Drawing and Visualization.

[19]  Kazuho Watanabe,et al.  Spectral-Based Contractible Parallel Coordinates , 2014, 2014 18th International Conference on Information Visualisation.

[20]  Kang Zhang,et al.  Unlocking the Complexity of Port Data With Visualization , 2015, IEEE Transactions on Human-Machine Systems.

[21]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[22]  Leland Wilkinson,et al.  ScagExplorer: Exploring Scatterplots by Their Scagnostics , 2014, 2014 IEEE Pacific Visualization Symposium.

[23]  Daniel Weiskopf,et al.  State of the Art of Parallel Coordinates , 2013, Eurographics.

[24]  Ken Wakita,et al.  Interactive high-dimensional visualization of social graphs , 2015, 2015 IEEE Pacific Visualization Symposium (PacificVis).

[25]  Roberto Tamassia,et al.  Handbook on Graph Drawing and Visualization , 2013 .

[26]  Ulrik Brandes,et al.  Graph Drawing by Classical Multidimensional Scaling: New Perspectives , 2012, GD.

[27]  S. Lovell,et al.  Protein-protein interaction networks and biology—what's the connection? , 2008, Nature Biotechnology.

[28]  Ying Wang Florence,et al.  Enhancing SOM Based Visualization Methods for Better Data Navigation , 2013, ICONIP 2013.

[29]  Michael Burch,et al.  Graph Exploration by Multiple Linked Metric Views , 2014, 2014 18th International Conference on Information Visualisation.

[30]  Margherita Caccamo,et al.  Visual analysis of time-dependent multivariate data from dairy farming industry , 2014, 2014 International Conference on Information Visualization Theory and Applications (IVAPP).

[31]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

[32]  Masahiro Takatsuka,et al.  Enhancing SOM Based Visualization Methods for Better Data Navigation , 2013, ICONIP.

[33]  Yoshinobu Kawahara,et al.  Scatterplot layout for high-dimensional data visualization , 2015, J. Vis..

[34]  Yoshinobu Kawahara,et al.  Arrangement of Low-Dimensional Parallel Coordinate Plots for High-Dimensional Data Visualization , 2013, 2013 17th International Conference on Information Visualisation.

[35]  T. Kohonen,et al.  Coloring that reveals high-dimensional structures in data , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[36]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[37]  Stephen G. Kobourov,et al.  Force-Directed Drawing Algorithms , 2013, Handbook of Graph Drawing and Visualization.