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Volume: 29 | Article ID: art00012
A Visual Evaluation Study of Graph Sampling Techniques
  DOI :  10.2352/ISSN.2470-1173.2017.1.VDA-394  Published OnlineJanuary 2017

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.

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Fangyan Zhang, Song Zhang, Pak Chung Wong, Hugh Medal, Linkan Bian, J. Edward Swan II, T.J. Jankun-Kelly, "A Visual Evaluation Study of Graph Sampling Techniquesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis,  2017,  pp 110 - 117,

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