infoTheoreticLabeledE: Information functional for edge-labeled graphs
In QuACN: QuACN: Quantitative Analysis of Complex Networks

Description Usage Arguments Details Value Author(s) References Examples

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.

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

`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.

For details see the vignette.

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.

Michael Schutte

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

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")