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

Terrill L. Frantz (), Marcelo Cataldo () and Kathleen M. Carley ()
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Terrill L. Frantz: Carnegie Mellon University
Marcelo Cataldo: Two North Shore Center
Kathleen M. Carley: Carnegie Mellon University

Computational and Mathematical Organization Theory, 2009, vol. 15, issue 4, No 5, 303-328

Abstract: 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.

Keywords: Network topology; Data error; Measure robustness; Centrality; Observation error (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10588-009-9063-5

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