revolver {igraph}R Documentation

Measuring the driving force in evolving networks

Description

These functions assume a simple evolving network model and measure the functional form of a so-called attractiveness function governing the evolution of the network.

Usage

evolver.d (nodes, kernel, outseq = NULL, outdist = NULL, m = 1, 
           directed = TRUE)

revolver.d (graph, niter=5, sd=FALSE, norm=FALSE,
           cites=FALSE, expected=FALSE, error=TRUE, debug=numeric())
revolver.ad (graph, niter=5, agebins=max(vcount(graph)/7100, 10),
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE, error=TRUE,
            debug=matrix(ncol=2, nrow=0))
revolver.ade (graph, cats, niter=5, agebins=max(vcount(graph)/7100, 10),
             sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
             error=TRUE, debug=matrix(ncol=2, nrow=0))
revolver.e (graph, cats, niter=5, st=FALSE,
           sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
           error=TRUE, debug=numeric())
revolver.de (graph, cats, niter=5,
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.l (graph, niter=5, agebins=max(vcount(graph)/7100, 10),
           sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
           error=TRUE, debug=numeric())
revolver.dl (graph, niter=5, agebins=max(vcount(graph)/7100, 10),
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.el (graph, cats, niter=5, agebins=max(vcount(graph)/7100, 10),
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.r (graph, window, niter=5, sd=FALSE, norm=FALSE,
           cites=FALSE, expected=FALSE, error=TRUE, debug=numeric())
revolver.ar (graph, window, niter=5, agebins=max(vcount(graph)/7100, 10),
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE, error=TRUE,
            debug=matrix(ncol=2, nrow=0))
revolver.di (graph, cats, niter=5,
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.adi (graph, cats, niter=5, agebins=max(vcount(graph)/7100, 10),
             sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
             error=TRUE, debug=matrix(ncol=2, nrow=0))
revolver.il (graph, cats, niter=5, agebins=max(vcount(graph)/7100, 10),
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.ir (graph, cats, window, niter=5,
            sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
            error=TRUE, debug=numeric())
revolver.air (graph, cats, window,
             niter=5, agebins=max(vcount(graph)/7100, 10),
             sd=FALSE, norm=FALSE, cites=FALSE, expected=FALSE,
             error=TRUE, debug=matrix(ncol=2, nrow=0))
revolver.d.d (graph, vtime = V(graph)$time, etime = E(graph)$time, niter = 5, 
             sd = FALSE, norm = FALSE, cites = FALSE, expected = FALSE, 
             error = TRUE, debug = matrix(ncol = 2, nrow = 0))
revolver.p.p (graph, events = get.graph.attribute(graph, "events"), 
             vtime = V(graph)$time, etime = E(graph)$time, niter = 5, sd = FALSE, 
             norm = FALSE, cites = FALSE, expected = FALSE, error = TRUE, 
             debug = matrix(ncol = 2, nrow = 0)) 
revolver.error.d (graph, kernel)
revolver.error.ad (graph, kernel)
revolver.error.ade (graph, kernel, cats)
revolver.error.adi (graph, kernel, cats)
revolver.error.air (graph, kernel, cats, window)
revolver.error.ar (graph, kernel, window)
revolver.error.de (graph, kernel, cats)
revolver.error.di (graph, kernel, cats)
revolver.error.dl (graph, kernel)
revolver.error.e (graph, kernel, cats)
revolver.error.el (graph, kernel, cats)
revolver.error.il (graph, kernel, cats)
revolver.error.ir (graph, kernel, cats, window)
revolver.error.l (graph, kernel)
revolver.error.r (graph, kernel, window)

revolver.ml.ade (graph, niter, cats, agebins = 300, delta = 1e-10,
    filter = NULL)
revolver.ml.d (graph, niter, delta = 1e-10, filter = NULL)
revolver.ml.de (graph, niter, cats, delta = 1e-10, filter = NULL)
revolver.ml.df (graph, niter, delta = 1e-10)
revolver.ml.f (graph, niter, delta = 1e-10)
revolver.ml.l (graph, niter, agebins = 300, delta = 1e-10)

revolver.ml.AD.alpha.a.beta (graph, alpha, a, beta, abstol = 1e-08,
    reltol = 1e-08, maxit = 1000, agebins = 300, filter = NULL)
revolver.ml.AD.dpareto (graph, alpha, a, paralpha, parbeta, parscale,
    abstol = 1e-08, reltol = 1e-08, maxit = 1000, agebins = 300, filter
    = NULL)
revolver.ml.ADE.alpha.a.beta (graph, cats, alpha, a, beta, coeffs,
    abstol = 1e-08, reltol = 1e-08, maxit = 1000, agebins = 300, filter
    = NULL)
revolver.ml.ADE.dpareto (graph, cats, alpha, a, paralpha, parbeta, parscale, 
    coeffs, abstol = 1e-08, reltol = 1e-08, maxit = 1000, agebins = 300, 
    filter = NULL)
revolver.ml.D.alpha (graph, alpha, abstol = 1e-08, reltol = 1e-08, maxit
    = 1000, filter = NULL)
revolver.ml.D.alpha.a (graph, alpha, a, abstol = 1e-08, reltol = 1e-08,
    maxit = 1000, filter = NULL)
revolver.ml.DE.alpha.a (graph, cats, alpha, a, coeffs, abstol = 1e-08,
    reltol = 1e-08, maxit = 1000, filter = NULL)

revolver.ml.AD.dpareto.eval (graph, alpha, a, paralpha, parbeta,
    parscale, agebins = 300, filter = NULL) 
revolver.ml.ADE.dpareto.eval (graph, cats, alpha, a, paralpha, parbeta,
    parscale, coeffs, agebins = 300, filter = NULL) 
revolver.ml.ADE.dpareto.evalf (graph, cats, par, agebins, filter = NULL)

revolver.probs.ad (graph, kernel, ntk = FALSE)
revolver.probs.ade (graph, kernel, cats)
revolver.probs.d (graph, kernel, ntk = FALSE)
revolver.probs.de (graph, kernel, cats)
revolver.probs.ADE.dpareto (graph, par, cats, gcats, agebins)

Arguments

nodes

The number of vertices in the generated network.

kernel

The kernel function, a vector, matrix or array, depending on the number of model parameters.

outseq

The out-degree sequence, or NULL if no out-degree sequence is used.

outdist

The out-degree distribution, or NULL if all vertices have the same out-degree. This argument is ignored if the outseq argument is not NULL.

m

Numeric scalar, the out-degree of the verticec. It is ignored if at least one of outseq and outdist is not NULL.

directed

Logical scalar, whether to create a directed graph.

graph

The input graph.

niter

The number of iterations to perform.

sd

Logical scalar, whether to return the standard deviation of the estimates.

norm

Logical scalar, whether to return the normalizing factors.

cites

Logical scalar, whether to return the number of citations to the different vertex types.

expected

Logical scalar, whether to return the expected number of citations for the different vertex types.

error

Logical scalar, whether to return the error of the fit.

debug

Currently not used.

agebins

The number of bins for vertex age.

cats

The number of categories to use.

window

The width of the time window to use, measured in number of vertices.

vtime

Numeric vector, the time steps when the vertices where added to the network.

etime

Numeric vector, the time steps when the edges where added to the network.

events

A list of numeric vectors, each vector represents an event, with the participation of the listed vertices.

delta

Real scalar, the error margin that is allowed for the convergence.

filter

Logical vector, length is the number of vertices. Only vertices corresponding to TRUE entries are used in the fitting.

alpha

Starting value for the ‘alpha’ parameter.

a

Starting value for the ‘a’ parameter.

paralpha

Starting value for the ‘paralpha’ (Pareto alpha) parameter.

parbeta

Starting value for the ‘parbeta’ (Pareto beta) parameter.

parscale

Starting value for the ‘parscale’ (Pareto scale) parameter.

abstol

Real scalar, absolute tolerance for the ML fitting.

reltol

Real scalar, relative tolerance for the ML fitting.

maxit

Numeric scalar, the maximum number of iterations.

beta

Real scalar, starting value for the ‘beta’ parameter.

coeffs

Numeric vector, starting values for the coefficients.

par

Pareto parameters for the different vertex types, in a matrix.

ntk

Logical scalar, whether to return the Ntk values.

gcats

Numeric vector, the vertex types.

st

Logical scalar, whether to return the S(t) values.

Details

The functions should be considered as experimental, so no detailed documentation yet. Sorry.

Value

A named list.

Author(s)

Gabor Csardi csardi.gabor@gmail.com

[Package igraph version 0.7.0 Index]