QuACN-package: QuACN: Quantitative Analysis of Complex Networks
In QuACN: QuACN: Quantitative Analysis of Complex Networks
Description
Author(s)
References
Examples
Description
This package offers a set of topological network measures to analyze complex Networks structurally.
Author(s)
Laurin Mueller
References
L.A.J. Mueller, K.G. Kugler, A. Dander, A. Graber, M. Dehmer, QuACN: An R Package for Analyzing Complex Biological networks quantitatively, Bioinformatics, Vol. 27 no. 1 2011, pages 140-141.
Examples
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rm(list=ls())
library("graph")
library("RBGL")
library("QuACN")
###
set.seed(123)
g <- randomGraph(1:8, 1:5, 0.36, weights=FALSE)
###
mat.adj <- adjacencyMatrix(g)
mat.dist <- distanceMatrix(g)
vec.degree <- graph::degree(g)
ska.dia <- diameter(g)
ska.dia <- diameter(g,mat.dist)
##
wien <- wiener(g)
wiener(g,mat.dist)
##
harary(g)
harary(g,mat.dist)
##
balabanJ(g)
balabanJ(g,mat.dist)
##
meanDistanceDeviation(g)
meanDistanceDeviation(g,mat.dist)
##
compactness(g)
compactness(g,mat.dist)
compactness(g,mat.dist,wiener(g,mat.dist))
##
productOfRowSums(g,log=FALSE)
productOfRowSums(g,log=TRUE)
productOfRowSums(g,mat.dist,log=FALSE)
productOfRowSums(g,mat.dist,log=TRUE)
##
hyperDistancePathIndex(g)
hyperDistancePathIndex(g,mat.dist)
hyperDistancePathIndex(g,mat.dist,wiener(g,mat.dist))
##
totalAdjacency(g)
totalAdjacency(g,mat.adj)
##
zagreb1(g)
zagreb1(g,vec.degree)
##
zagreb2(g)
zagreb2(g,vec.degree)
##
randic(g)
randic(g,vec.degree)
##
normalizedEdgeComplexity(g)
normalizedEdgeComplexity(g,totalAdjacency(g,mat.adj))
##
complexityIndexB(g)
complexityIndexB(g,mat.dist)
complexityIndexB(g,mat.dist,vec.degree)
##
degreeDistribution(g)
degreeDistribution(g,vec.degree)
##
numNodes(g)
numEdges(g)
##
localClusteringCoeff(g)
localClusteringCoeff(g,vec.degree)
##
topologicalInfoContent(g)
topologicalInfoContent(g,mat.dist)
topologicalInfoContent(g,mat.dist,vec.degree)
##
bertz(g)
bertz(g,mat.dist)
bertz(g,mat.dist,vec.degree)
##
bonchev1(g)
bonchev1(g,mat.dist)
##
bonchev2(g)
bonchev2(g,mat.dist)
bonchev2(g,mat.dist,wiener(g))
##
radialCentric(g)
radialCentric(g,mat.dist)
##
vertexDegree(g)
vertexDegree(g,vec.degree)
##
balabanlike1(g)
balabanlike1(g,mat.dist)
##
balabanlike2(g)
balabanlike2(g,mat.dist)
##
graphVertexComplexity(g)
graphVertexComplexity(g,mat.dist)
##
infoTheoreticGCM(g)
infoTheoreticGCM(g,mat.dist,coeff="lin",infofunct="sphere",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="exp",infofunct="sphere",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="const",infofunct="pathlength",lambda=4000)
infoTheoreticGCM(g,mat.dist,coeff="quad",infofunct="vertcent",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="lin",infofunct="degree",lambda=1000)
##
eigenvalueBased(g,adjacencyMatrix,2)
eigenvalueBased(g,laplaceMatrix,2)
eigenvalueBased(g,distanceMatrix,2)
eigenvalueBased(g,distancePathMatrix,2)
eigenvalueBased(g,augmentedMatrix,2)
eigenvalueBased(g,extendedAdjacencyMatrix,2)
eigenvalueBased(g,vertConnectMatrix,2)
eigenvalueBased(g,randomWalkMatrix,2)
eigenvalueBased(g,weightStrucFuncMatrix_lin,2)
eigenvalueBased(g,weightStrucFuncMatrix_exp,2)
QuACN documentation built on May 2, 2019, 5:46 p.m.
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Description Author(s) References Examples
Description
This package offers a set of topological network measures to analyze complex Networks structurally.
Author(s)
Laurin Mueller
References
L.A.J. Mueller, K.G. Kugler, A. Dander, A. Graber, M. Dehmer, QuACN: An R Package for Analyzing Complex Biological networks quantitatively, Bioinformatics, Vol. 27 no. 1 2011, pages 140-141.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | rm(list=ls())
library("graph")
library("RBGL")
library("QuACN")
###
set.seed(123)
g <- randomGraph(1:8, 1:5, 0.36, weights=FALSE)
###
mat.adj <- adjacencyMatrix(g)
mat.dist <- distanceMatrix(g)
vec.degree <- graph::degree(g)
ska.dia <- diameter(g)
ska.dia <- diameter(g,mat.dist)
##
wien <- wiener(g)
wiener(g,mat.dist)
##
harary(g)
harary(g,mat.dist)
##
balabanJ(g)
balabanJ(g,mat.dist)
##
meanDistanceDeviation(g)
meanDistanceDeviation(g,mat.dist)
##
compactness(g)
compactness(g,mat.dist)
compactness(g,mat.dist,wiener(g,mat.dist))
##
productOfRowSums(g,log=FALSE)
productOfRowSums(g,log=TRUE)
productOfRowSums(g,mat.dist,log=FALSE)
productOfRowSums(g,mat.dist,log=TRUE)
##
hyperDistancePathIndex(g)
hyperDistancePathIndex(g,mat.dist)
hyperDistancePathIndex(g,mat.dist,wiener(g,mat.dist))
##
totalAdjacency(g)
totalAdjacency(g,mat.adj)
##
zagreb1(g)
zagreb1(g,vec.degree)
##
zagreb2(g)
zagreb2(g,vec.degree)
##
randic(g)
randic(g,vec.degree)
##
normalizedEdgeComplexity(g)
normalizedEdgeComplexity(g,totalAdjacency(g,mat.adj))
##
complexityIndexB(g)
complexityIndexB(g,mat.dist)
complexityIndexB(g,mat.dist,vec.degree)
##
degreeDistribution(g)
degreeDistribution(g,vec.degree)
##
numNodes(g)
numEdges(g)
##
localClusteringCoeff(g)
localClusteringCoeff(g,vec.degree)
##
topologicalInfoContent(g)
topologicalInfoContent(g,mat.dist)
topologicalInfoContent(g,mat.dist,vec.degree)
##
bertz(g)
bertz(g,mat.dist)
bertz(g,mat.dist,vec.degree)
##
bonchev1(g)
bonchev1(g,mat.dist)
##
bonchev2(g)
bonchev2(g,mat.dist)
bonchev2(g,mat.dist,wiener(g))
##
radialCentric(g)
radialCentric(g,mat.dist)
##
vertexDegree(g)
vertexDegree(g,vec.degree)
##
balabanlike1(g)
balabanlike1(g,mat.dist)
##
balabanlike2(g)
balabanlike2(g,mat.dist)
##
graphVertexComplexity(g)
graphVertexComplexity(g,mat.dist)
##
infoTheoreticGCM(g)
infoTheoreticGCM(g,mat.dist,coeff="lin",infofunct="sphere",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="exp",infofunct="sphere",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="const",infofunct="pathlength",lambda=4000)
infoTheoreticGCM(g,mat.dist,coeff="quad",infofunct="vertcent",lambda=1000)
infoTheoreticGCM(g,mat.dist,coeff="lin",infofunct="degree",lambda=1000)
##
eigenvalueBased(g,adjacencyMatrix,2)
eigenvalueBased(g,laplaceMatrix,2)
eigenvalueBased(g,distanceMatrix,2)
eigenvalueBased(g,distancePathMatrix,2)
eigenvalueBased(g,augmentedMatrix,2)
eigenvalueBased(g,extendedAdjacencyMatrix,2)
eigenvalueBased(g,vertConnectMatrix,2)
eigenvalueBased(g,randomWalkMatrix,2)
eigenvalueBased(g,weightStrucFuncMatrix_lin,2)
eigenvalueBased(g,weightStrucFuncMatrix_exp,2)
|
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