Empirical Characterization of Graph Sampling Algorithms

TL;DR

This paper empirically evaluates five graph sampling algorithms across various graph properties and graph types, revealing no universally best method and highlighting the effectiveness of neighborhood-exploring strategies.

Contribution

It provides a comprehensive empirical analysis of multiple graph sampling algorithms on diverse graph properties and types, offering insights into their relative performance.

Findings

No single sampling method accurately preserves all graph properties.

Neighborhood-exploring algorithms outperform others in property preservation.

Sampling effectiveness varies across graph types and properties.

Abstract

Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assortativity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological, and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.