Snowball sampling from graphs
Methodology
2023-05-24 v3 Statistics Theory
Statistics Theory
Abstract
We develop unbiased strategies to probabilistic T-wave snowball sampling from graphs, where the interest of estimation may concern finite-order subgraphs such as triangles, cycles or stars. Our approaches encompass also the finite-population sampling strategies to multiplicity sampling and adaptive cluster sampling, both of which can be recast as snowball sampling aimed at graph node totals. A general snowball sampling theory offers greater flexibility in terms of scope and efficiency of graph sampling, in addition to the existing random node or edge sampling methods.
Cite
@article{arxiv.2003.09467,
title = {Snowball sampling from graphs},
author = {Melike Oguz-Alper and Li-Chun Zhang},
journal= {arXiv preprint arXiv:2003.09467},
year = {2023}
}
Comments
One sentence on page 17 (highlighted) of previous version was supposed to have been replaced, as in this version