Abstract
This article introduces a confidence level (CL) statistic to accompany the identification of the most central actor in relational, social network data. CL is the likelihood that the most-central actor assertion is correct in light of imperfect network data. The CL value is derived from a frequency-based probability according to perturbed samples of feature-equivalent network data. Analysts often focus attention towards the most central, highest valued, top actor [or node] according to one of four traditional measures: degree, betweenness, closeness or eigenvector centrality. However, given that collected social network data often has missing relational links, the correctness of the top-actor claim becomes uncertain. This paper describes and illustrates a practical approach for estimating and applying a CL to the top-actor identification task. We provide a simple example of the technique used to derive a posterior probability, then apply the same approach to larger, more pragmatic random network by using the results of an extensive virtual experiment involving uniform random and scale-free topologies. This article has implications in organizational practice and theory; it is simple and lays groundwork for developing more intricate estimates of reliability for other network measures.
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Frantz, T.L., Carley, K.M. Reporting a network’s most-central actor with a confidence level. Comput Math Organ Theory 23, 301–312 (2017). https://doi.org/10.1007/s10588-016-9229-x
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DOI: https://doi.org/10.1007/s10588-016-9229-x