Abstract
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.
| Original language | English |
|---|---|
| Article number | 6212 |
| Journal | Scientific Reports |
| Volume | 4 |
| DOIs | |
| Publication status | Published - 28 Aug 2014 |
Bibliographical note
Funding Information:The authors thank Thilo Gross and Naoki Masuda for useful discussions, and Seung-Kyu Shin for assistance with data curation. This work was supported by the National Research Foundation of Korea funded by the Korean government (NRF-20100004910 and NRF-2013S1A3A2055285), Korea Advanced Institute of Science & Technology, Kyung Hee University (Grant KHU-201020100116), BK21 Plus Program for Content Science, and the IT R&D program of MSIP/KEIT [10045459].