Statistical properties of sampled networks

TL;DR

This paper analyzes how different sampling methods affect the estimation of topological properties in scale-free networks, highlighting biases and proposing criteria to improve accuracy.

Contribution

It introduces a comparative study of sampling methods and explains the biases in estimating network properties, providing guidelines to mitigate errors.

Findings

Sampling methods cause significant estimation biases.

Different properties are affected differently by sampling.

Criteria are proposed to reduce estimation errors.

Abstract

We study the statistical properties of the sampled scale-free networks, deeply related to the proper identification of various real-world networks. We exploit three methods of sampling and investigate the topological properties such as degree and betweenness centrality distribution, average path length, assortativity, and clustering coefficient of sampled networks compared with those of original networks. It is found that the quantities related to those properties in sampled networks appear to be estimated quite differently for each sampling method. We explain why such a biased estimation of quantities would emerge from the sampling procedure and give appropriate criteria for each sampling method to prevent the quantities from being overestimated or underestimated.