J. Edward Swan II

A Visual Evaluation Study of Graph Sampling Techniques

Fangyan Zhang, Song Zhang, Pak Chung Wong, Hugh Medal, Linkan Bian, J. Edward Swan II, and T. J. Jankun-Kelly. A Visual Evaluation Study of Graph Sampling Techniques. In IS&T International Symposium on Electronic Imaging 2017, Visualization and Data Analysis 2017, pp. 110–117, Society for Imaging Science and Technology, January 2017. 10.2352/ISSN.2470-1173.2017.1.VDA-394

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Abstract

We evaluate a dozen prevailing graph-sampling techniques with anultimate goal to better visualize and understand big and complexgraphs that exhibit different properties and structures. Theevaluation uses eight benchmark datasets with four different graphtypes collected from Stanford Network Analysis Platform and NetworkXto give a comprehensive comparison of various types of graphs. Thestudy provides a practical guideline for visualizing big graphs ofdifferent sizes and structures. The paper discusses results andimportant observations from the study.

BibTeX

@InProceedings{VDA17-gst, 
  author =      {Fangyan Zhang and Song Zhang and Pak Chung Wong and Hugh Medal and
                 Linkan Bian and J. Edward {Swan~II} and T. J. Jankun-Kelly},
  title =       {A Visual Evaluation Study of Graph Sampling Techniques},
  booktitle =   {{IS\&T} International Symposium on Electronic Imaging 2017,
                 Visualization and Data Analysis 2017},
  year =        2017, 
  location =    {Burlingame, CA, USA}, 
  publisher =   {Society for Imaging Science and Technology},
  date =        {January 29}, 
  month =       {January}, 
  pages =       {110--117}, 
  note =        {10.2352/ISSN.2470-1173.2017.1.VDA-394},
  abstract =    { 
We evaluate a dozen prevailing graph-sampling techniques with an
ultimate goal to better visualize and understand big and complex
graphs that exhibit different properties and structures. The
evaluation uses eight benchmark datasets with four different graph
types collected from Stanford Network Analysis Platform and NetworkX
to give a comprehensive comparison of various types of graphs. The
study provides a practical guideline for visualizing big graphs of
different sizes and structures. The paper discusses results and
important observations from the study.
}, 
}