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Robustness of centrality measures under uncertainty: Examining the role of network topology

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Abstract

This study investigates the topological form of a network and its impact on the uncertainty entrenched in descriptive measures computed from observed social network data, given ubiquitous data-error. We investigate what influence a network’s topology, in conjunction with the type and amount of error, has on the ability of a measure, derived from observed data, to correctly approximate the same of the ground-truth network. By way of a controlled experiment, we reveal the differing effect that observation error has on measures of centrality and local clustering across several network topologies: uniform random, small-world, core-periphery, scale-free, and cellular. Beyond what is already known about the impact of data uncertainty, we found that the topology of a social network is, indeed, germane to the accuracy of these measures. In particular, our experiments show that the accuracy of identifying the prestigious, or key, actors in a network—according observed data—is considerably predisposed by the topology of the ground-truth network.

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Authors and Affiliations

  1. Center for Computational Analysis of Social and Organizational Systems (CASOS), Institute for Software Research, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA

    Terrill L. Frantz & Kathleen M. Carley

  2. Two North Shore Center, Suite 320, Pittsburgh, PA, 15212, USA

    Marcelo Cataldo

Authors
  1. Terrill L. Frantz
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  2. Marcelo Cataldo
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  3. Kathleen M. Carley
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Correspondence to Terrill L. Frantz.

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Frantz, T.L., Cataldo, M. & Carley, K.M. Robustness of centrality measures under uncertainty: Examining the role of network topology. Comput Math Organ Theory 15, 303–328 (2009). https://doi.org/10.1007/s10588-009-9063-5

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