A Survey and Taxonomy of Graph Sampling
Pili Hu, Wing Cheong Lau

TL;DR
This paper provides a comprehensive survey and taxonomy of graph sampling techniques, analyzing their objectives, approaches, properties preserved, and implications for graph analysis and algorithm acceleration.
Contribution
It introduces a formal taxonomy and framework linking theoretical analysis with practical sampling methods, highlighting gaps and needs for systematic evaluation.
Findings
Classifies graph sampling methods into distinct categories.
Identifies which graph properties are preserved by different sampling techniques.
Highlights the need for systematic evaluation in graph sampling research.
Abstract
Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. It has a wide spectrum of applications, e.g. survey hidden population in sociology [54], visualize social graph [29], scale down Internet AS graph [27], graph sparsification [8], etc. In some scenarios, the whole graph is known and the purpose of sampling is to obtain a smaller graph. In other scenarios, the graph is unknown and sampling is regarded as a way to explore the graph. Commonly used techniques are Vertex Sampling, Edge Sampling and Traversal Based Sampling. We provide a taxonomy of different graph sampling objectives and graph sampling approaches. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided. Although being smaller in size, sampled graphs may be similar to original graphs in…
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