dc.contributor.author | Arnold, NA | en_US |
dc.contributor.author | Mondragón, RJ | en_US |
dc.contributor.author | Clegg, RG | en_US |
dc.date.accessioned | 2024-01-15T15:52:51Z | |
dc.date.issued | 2023-12-01 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/93908 | |
dc.description.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. | en_US |
dc.relation.ispartof | Applied Network Science | en_US |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Using a Bayesian approach to reconstruct graph statistics after edge sampling | en_US |
dc.type | Article | |
dc.identifier.doi | 10.1007/s41109-023-00574-3 | en_US |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 8 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |