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Title: Graph Sampling for Visual Analytics

Abstract

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, the size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1406791
Report Number(s):
PNNL-SA-122634
Journal ID: ISSN 1062-3701; 453040300
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Imaging Science and Technology; Journal Volume: 61; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Big graphs; Graph sampling; Graph properties; Graph drawing; Visualization

Citation Formats

Zhang, Fangyan, Zhang, Song, and Chung Wong, Pak. Graph Sampling for Visual Analytics. United States: N. p., 2017. Web. doi:10.2352/J.ImagingSci.Technol.2017.61.4.040503.
Zhang, Fangyan, Zhang, Song, & Chung Wong, Pak. Graph Sampling for Visual Analytics. United States. doi:10.2352/J.ImagingSci.Technol.2017.61.4.040503.
Zhang, Fangyan, Zhang, Song, and Chung Wong, Pak. Sat . "Graph Sampling for Visual Analytics". United States. doi:10.2352/J.ImagingSci.Technol.2017.61.4.040503.
@article{osti_1406791,
title = {Graph Sampling for Visual Analytics},
author = {Zhang, Fangyan and Zhang, Song and Chung Wong, Pak},
abstractNote = {Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, the size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.},
doi = {10.2352/J.ImagingSci.Technol.2017.61.4.040503},
journal = {Journal of Imaging Science and Technology},
number = 4,
volume = 61,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}