Using a Bayesian approach to reconstruct graph statistics after edge sampling
Abstract
Often, due to prohibitively large size or to limits to data collecting APIs, it is not possible to work with a complete network dataset and sampling is required. A type of sampling which is consistent with Twitter API restrictions is uniform edge sampling. In this paper, we propose a methodology for the recovery of two fundamental network properties from an edge-sampled network: the degree distribution and the triangle count (we estimate the totals for the network and the counts associated with each edge). We use a Bayesian approach and show a range of methods for constructing a prior which does not require assumptions about the original network. Our approach is tested on two synthetic and three real datasets with diverse sizes, degree distributions, degree-degree correlations and triangle count distributions.
Cite
@article{arxiv.2207.11793,
title = {Using a Bayesian approach to reconstruct graph statistics after edge sampling},
author = {Naomi A. Arnold and Raul J. Mondragon and Richard G. Clegg},
journal= {arXiv preprint arXiv:2207.11793},
year = {2023}
}
Comments
Extended version of the paper accepted in Complex Networks 2022