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Here A G and D G are, as are seen in Theorem 2, the adjacency and degree matrices, respectively.
As per the type of network then it calculates all required statistical parameters from adjacency A, Laplacian L and degree matrices D and similarly for weighted matrices i.e. (tilde {A},tilde {L},) and (tilde {D}) etc.
D S and D G are the degree matrices of S and G.
Let and be the affinity matrices of the genetic interaction network (e.g. epistatic effect between SNPs) and network of traits (e.g. PPI network or gene co-expression network), and and be their degree matrices.
The node degree matrices D d and D p are two diagonal matrices with their (k, k -element defined as D d (k -element m = 1 n defined, m asD d p (k, k ) = ∑ m = 1 n p w p k, m.
Similar(55)
We consider a bipartite version of the color degree matrix problem.
We give necessary and sufficient conditions for a bipartite degree matrix (also known as demand matrix) to be the color degree matrix of an edge-disjoint union of half-regular graphs.
We also give necessary and sufficient perturbations to transform realizations of a half-regular degree matrix into each other.
Secondly, combined with the realization mechanism of membership function in fuzzy set theory, the confidence distance and the support degree matrix are designed to evaluate the support degree among multi-sensor observations.
where is the degree matrix of.
The degree matrix D is a matrix with diagonal elements equalling either 0 or 1.
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