Estimating network structure via random sampling: cognitive social structures and the adaptive threshold method

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dc.citation.epage600en_US
dc.citation.issueNumber4en_US
dc.citation.spage585en_US
dc.citation.volumeNumber34en_US
dc.contributor.authorSiciliano, M. D.en_US
dc.contributor.authorYenigun, D.en_US
dc.contributor.authorErtan, G.en_US
dc.date.accessioned2015-07-28T12:04:35Z
dc.date.available2015-07-28T12:04:35Z
dc.date.issued2012-10en_US
dc.departmentDepartment of Managementen_US
dc.description.abstractThis paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals' perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings.en_US
dc.identifier.doi10.1016/j.socnet.2012.06.004en_US
dc.identifier.issn0378-8733
dc.identifier.urihttp://hdl.handle.net/11693/13087
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.socnet.2012.06.004en_US
dc.source.titleSocial Networksen_US
dc.subjectNetwork samplingen_US
dc.subjectCognitive social structuresen_US
dc.subjectCross-network researchen_US
dc.subjectSocial networksen_US
dc.titleEstimating network structure via random sampling: cognitive social structures and the adaptive threshold methoden_US
dc.typeArticleen_US
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