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

Author

Listed:
  • Terrill L. Frantz

    (Peking University HSBC Business School)

  • Kathleen M. Carley

    (Carnegie Mellon University)

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.

Suggested Citation

  • Terrill L. Frantz & Kathleen M. Carley, 2017. "Reporting a network’s most-central actor with a confidence level," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 301-312, June.
    • Handle: RePEc:spr:comaot:v:23:y:2017:i:2:d:10.1007_s10588-016-9229-x
    DOI: 10.1007/s10588-016-9229-x
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    References listed on IDEAS

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    1. P.-J. Kim & H. Jeong, 2007. "Reliability of rank order in sampled networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(1), pages 109-114, January.
    2. Kathleen M. Carley, 2006. "Destabilization of covert networks," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 51-66, April.
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    Cited by:

    1. Zhang, Xin-Jie & Tang, Yong & Xiong, Jason & Wang, Wei-Jia & Zhang, Yi-Cheng, 2020. "Ranking game on networks: The evolution of hierarchical society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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