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Simulations to illustrate the applicability of our results to cyclic network topologies are also presented.
Yang et al. [103] proposed an HTL preprocessing method based on using a Directed Cyclic Network (DCN).
Using these coefficients, a directed cyclic network is built for each principal component to model the relationship amongst the domains and capture strong or weak relations amongst them.
This is the seminal idea in the Hopfield-Ninio theory of kinetic proofreading: The specificity is increased via cyclic network kinetics without altering molecular structures and equilibrium affinites.
This search engine includes two mechanisms: a diversity-guided three-phase velocity update law and restricted social best searching based on the cyclic network structure.
The principal component coefficients are used to form a directed cyclic network (DCN) where each node represents a domain (either source or target) and each node connection (edge weight) is the conditional dependence from one domain to another.
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It is worth mentioning that since the notion of degree makes sense only in the case of acyclic networks, special issues arise when networks contain cycles (see Antonelli (1997) for a treatment of inheritance on cyclic networks).
We then propose a convex semidefinite relaxation to approximately solve the (non-convex) edge-weight design problem for general (possibly cyclic) networks with nonidentical edge weights.
The BFS approach presented by [4] can be applied to cyclic networks, but the inferred hierarchical structure may contain conflicts in the level assignment.
Cyclic networks form a part of the intracellular signalling network of the cell.
It is then, however, still important to keep the weights to avoid non-unique results in cyclic networks.
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