Graph Sampling for Visual Analytics

Journal Article · Sat Jul 01 04:00:00 EDT 2017 · Journal of Imaging Science and Technology

DOI:https://doi.org/10.2352/J.ImagingSci.Technol.2017.61.4.040503· OSTI ID:1406791

Zhang, Fangyan; Zhang, Song; Chung Wong, Pak

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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1406791
• Report Number(s): PNNL-SA-122634; 453040300
• Journal Information: Journal of Imaging Science and Technology, Journal Name: Journal of Imaging Science and Technology Journal Issue: 4 Vol. 61; ISSN 1062-3701
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Big graphs
Graph drawing
Graph properties
Graph sampling
Visualization