infoTheoreticLabeledV1: V1 information functional for vertex-labeled graphs
In QuACN: QuACN: Quantitative Analysis of Complex Networks

Description Usage Arguments Value Author(s) References Examples

This method assigns a probability value to each vertex of the network using the V1 information functional for vertex-labeled graphs. It is based on the same principles as infoTheoreticGCM.

1 2	infoTheoreticLabeledV1(g, dist=NULL, coeff="lin", custCoeff=NULL, coeffMatrix=NULL, lambda=1000)

`g`	a graph as a graphNEL object. Each vertex must have an "atom" data attribute specifying its atomic number or chemical symbol.
`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".
`coeffMatrix`	overrides the "coeff" and "custCoeff" parameters to set entirely user-defined coefficients for each pair of chemical symbol (columns) and distance from the focussed vertex (rows). The columns have to be named after the chemical symbols.
`lambda`	specifies the scaling constant for the distance measures. The default value is 1000.

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)

nodeDataDefaults(g, "atom") <- "C"
nodeData(g, "2", "atom") <- "O"

infoTheoreticLabeledV1(g)