Robustness of centrality measures under uncertainty: Examining the role of network topology
| Autor: | Kathleen M. Carley, Marcelo Cataldo, Terrill L. Frantz |
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| Rok vydání: | 2009 |
| Předmět: |
Dynamic network analysis
General Computer Science Applied Mathematics General Decision Sciences Network science Network theory computer.software_genre Network topology Computational Mathematics Robustness (computer science) Modeling and Simulation Data mining Controlled experiment Cluster analysis Centrality computer Mathematics |
| Zdroj: | Computational and Mathematical Organization Theory. 15:303-328 |
| ISSN: | 1572-9346 1381-298X |
| DOI: | 10.1007/s10588-009-9063-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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