A Visual and Statistical Benchmark for Graph Sampling Methods

Effectively visualizing large graphs is challenging. Capturing the statistical properties of these large graphs is also difficult. Sampling algorithms, developed to more feasibly observe and analyze large graphs, are indispensable for this task. Many sampling approaches for graph simplification have been proposed. These methods can be grouped into three categories: node sampling, edge sampling, and traversal-based sampling. It is still an open question, however, which single sampling technique produces the best representative sample. The goal of this paper is to evaluate commonly used sampling methods through a combined visual and statistical comparison. Initial results indicate that the effectiveness of a sampling method is dependent on the type of graph, the size of the graph, and the desired statistical property. The benchmark can be used as a guideline in choosing the proper method for a particular graph sampling task. The resulting benchmark can be incorporated into graph visualization and analysis tools.