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A special case of a corporative ranking network is the kingdom ranking network, in which each group is identified by a common biological linkage, such as matriline or common genetic descent.
Based upon the optimal ranking network, we list in Table 2 the rank coordinates computed for the members of each matriline in the macaque enclosure (Cage 5).
The cost of the corresponding optimal ranking network, using relation (1), is computed to be 55.02.
We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques.
The latter groups' memberships are very essential for this non-sequential ranking network because they constitute the network's backbone structure.
This ranking network is common in human society, but has rarely been studied using social network analysis [10].
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Section 3 shows indices for ranking networks' switches.
Further, TOPSIS was found to be more precise than SAW in ranking networks.
In the future, he tells us, privacy will be dead and our blazingly public lives will be broadcast by transparent ranking networks (think Klout and Peer Index on steroids).
Finally, we briefly comment on the limits of the maximum likelihood estimate (MLE) approach and Bayesian analysis in constructing corporative ranking networks.
We propose a set of three steps, each of which is designed to resolve a significant challenge encountered in the construction of real-world ranking networks.
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