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arXiv · 2207.11793

Using a Bayesian approach to reconstruct graph statistics after edge sampling

Naomi A. Arnold·Raul J. Mondragon·Richard G. Clegg
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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.

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Naomi A. Arnold, Raul J. Mondragon, Richard G. Clegg. 2022-07-24. Using a Bayesian approach to reconstruct graph statistics after edge sampling. https://arxiv.org/abs/2207.11793

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