Bayesian Inference of Natural Rankings in Incomplete Competition Networks

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Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest - essential in determining reward and penalty - is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the "Natural Ranking," an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2014-08
Language
English
Article Type
Article
Citation

SCIENTIFIC REPORTS, v.4

ISSN
2045-2322
DOI
10.1038/srep06212
URI
http://hdl.handle.net/10203/193156
Appears in Collection
GCT-Journal Papers(저널논문)
Files in This Item
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