infoTheoreticLabeledE: Information functional for edge-labeled graphs

Description Usage Arguments Details Value Author(s) References Examples

Description

This method assigns a probability value to each vertex of the network using an information functional for edge-labeled graphs. It is based on the same principles as infoTheoreticGCM.

Usage

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infoTheoreticLabeledE(g, dist=NULL, coeff="lin", custCoeff=NULL, lambda=1000)

Arguments

g

a graph as a graphNEL object. Each edge must have a "bond" data attribute specifying its conventional bond order (1, 2, 3 or 1.5 for single, double, triple and aromatic bonds, respectively).

dist

the distance matrix of the graph. Will be automatically calculated if not supplied.

coeff

specifies the weighting coefficients. Possible values are "lin" (default), "quad", "exp", "const" or "cust". If it is set to "cust" you have to specify your customized weighting schema with the parameter custCoeff.

custCoeff

specifies the customized weighting scheme. To use it you need to set coeff="cust".

lambda

specifies the scaling constant for the distance measures. The default value is 1000.

Details

For details see the vignette.

Value

The returned list consists of the following items:

entropy

contains the calculated entropy measure.

distance

contains the calculated distance measure.

pis

contains the calculated probability distribution.

fvi

contains the calculated values of the functional for each vertex.

Author(s)

Michael Schutte

References

M. Dehmer, N. Barbarini, K. Varmuza, and A. Graber. Novel topological descriptors for analyzing biological networks. BMC Structural Biology, 10:18, 2010.

Examples

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set.seed(987)
g <- randomEGraph(as.character(1:10), 0.3)

edgeDataDefaults(g, "bond") <- 1
edgeData(g, "1", "6", "bond") <- 3
edgeData(g, "2", "8", "bond") <- 2

infoTheoreticLabeledE(g, coeff="exp")


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