Eigenvalue-based Descriptors

Description

Eigenvalue-based Descriptors

Usage

1
 eigenvalueBased(g, matrix_function, s=1)

Arguments

g

A graph as a graphNEL object.

matrix_function

The matrix function to calculate the desired matrix for the graph. For details see the vignette or the example section below.

s

Parameter to caluclate the descriptors, see reference. Default set to 1.

Details

For details see the Vignette.

Value

It returns a list with following items:

HMs

Formula (2) in the reference paper.

SMstance

Formula (3) in the reference paper.

ISMs

Formula (4) in the reference paper.

PMs

Formula (5) in the reference paper.

IPMs

Formula (6) in the reference paper.

Author(s)

Lavanya Sivakumar, Laurin Mueller

References

Dehmer M, Sivakumar L, Varmuzua K: Uniquely Discriminating Molecular Structures Using Novel Eigenvalue Based Descriptors. match 2012, 67:147-172

Examples

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library(RBGL)
set.seed(123)
g <- randomGraph(1:8, 1:5, 0.36, weights=FALSE)
mat.dist <- distanceMatrix(g)

##Matrices like in the paper
##1. adjacency matrix
eigenvalueBased(g,adjacencyMatrix,2)
##2. Laplacian matrix
eigenvalueBased(g,laplaceMatrix,2)
##3. Distance matrix
eigenvalueBased(g,distanceMatrix,2)
##4. Distance path Matrix
eigenvalueBased(g,distancePathMatrix,2)
##5. Augmented vertex degree matrix
eigenvalueBased(g,augmentedMatrix,2)
##6. Extended adjacency matrix
eigenvalueBased(g,extendedAdjacencyMatrix,2)
##7. Connectivity matrix 
eigenvalueBased(g,vertConnectMatrix,2) 
##8. Random Walk markov matrix 
eigenvalueBased(g,randomWalkMatrix,2)  
##9. Weighted structure function matrix IM1
eigenvalueBased(g,weightStrucFuncMatrix_lin,2) 
##10. Weighted structure function matrix IM2
eigenvalueBased(g,weightStrucFuncMatrix_exp,2)

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