NetPreProc (version 1.2)

Prob.norm-methods: Probabilistic normalization of a graph

Description

Method to compute the transition probability matrix of network. A network matrix is normalized by dividing each entry \(W_{ij}\) by the the sum of elements of row \(i\) In other words if \(D\) is a diagonal matrix such that \(D_{ii} = \sum_j W_{ij}\) then the normalize matrix is: $$ W_{norm} = D^{-1} W $$

Usage

Prob.norm(W)

Value

The normalized transition probability matrix of network

Arguments

W

an object representing the graph to be normalized

Methods

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

Examples

library(bionetdata);
# making transition prob matrix for a drug-drug similarity network
data(DD.chem.data);
W <- Prob.norm(DD.chem.data);
# the same using an object of class graphAM and of class graphNEL
# \donttest{
g <- new("graphAM", adjMat=DD.chem.data, values=list(weight=DD.chem.data));
Wg <- Prob.norm(g);
g2 <- as(g, "graphNEL");
Wg2 <- Prob.norm(g2);# }