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Reporting a network’s most-central actor with a confidence level

by Terrill L. FRANTZ* & Kathleen M. Carley

ARTICLE | Computational and Mathematical Organization Theory | Vol. 23, 2017


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.TheCLvalueisderivedfromafrequency-basedprobabilityaccordingtoperturbed
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
actor claim becomes uncertain. This paper describes and illustrates a practical approach forestimatingandapplyingaCLtothetop-actoridentificationtask.Weprovideasimple
example of the technique used to derive a posterior probability, then apply the same
approachto larger,more pragmatic random network by using the results ofan extensive
virtual experimentinvolvinguniform 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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