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Estimating Network Structure via Random Sampling: Cognitive Social Structures and the Adaptive Threshold Method

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journal contribution
posted on 03.12.2013, 00:00 by Michael Siciliano, Deniz Yenigun, Gunes Ertan
This 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.


Publisher Statement

NOTICE: This is the author’s version of a work that was accepted for publication in Social Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Social Networks, [Vol 34, Issue 4, 2012] DOI: 10.1016/j.socnet.2012.06.004







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