Laplacian.norm-methods | R Documentation |
Methods to normalize weights of square symmetric adjacency matrices. A network matrix is normalized by dividing each entry W_{ij} by the square root of the product of the sum of elements of row i and the sum of the elemnts in column j. In other words if D is a diagonal matrix such that D_{ii} = ∑_j W_{ij}, then the normalize matrix is:
W_{norm} = D^{-1/2} W D^{-1/2}
Laplacian.norm(W)
W |
an object representing the graph to be normalized |
The normalized adjacency matrix of the network
signature(W = "graph")
an object of the virtual class graph (hence including objects of class graphAM
and graphNEL
from the package graph)
signature(W = "matrix")
a matrix representing the adjacency matrix of the graph
library(bionetdata); # normalization of drug-drug similarity networks data(DD.chem.data); W <- Laplacian.norm(DD.chem.data); # the same using an object of class graphAM g <- new("graphAM", adjMat=DD.chem.data, values=list(weight=DD.chem.data)); Wg <- Laplacian.norm(g);
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