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.
Revised: June 5, 2018 |
Published: January 29, 2017
Citation
Zhang F., S. Zhang, P.C. Wong, H. Medal, L. Bian, J. Swan II, and T. Jankun-Kelly. 2017.A Visual Evaluation Study of Graph Sampling Techniques. In IS&T International Symposium on Electronic Imaging Science and Technology: Visualization and Data Analysis 2017, January 29-February 2, 2017, Burlingame, CA, edited by T Wischgoll, S Xhang and D Kao, 110-117. Springfield, Virginia:Society for Imaging Science and Technology.PNNL-SA-122748.doi:10.2352/ISSN.2470-1173.2017.1.VDA-394