measure.dynamics {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

measure.dynamics.idage (graph, agebins = 300, iterations = 5, 
    error=TRUE, time.window = NULL, number = FALSE, cites=FALSE,
    norm.method="old")
measure.dynamics.id(graph, iterations = 5, error=TRUE, 
    time.window = NULL, number = FALSE, cites=FALSE, norm.method="old",
    debug=FALSE, debugdeg=0, which=2) 
measure.dynamics.d.d(graph, vtime, etime, iterations = 5)
measure.dynamics.citedcat.id.age(graph, categories, agebins = 300,
    iterations = 5, norm = c(1, 1, 1)) 
measure.dynamics.citingcat.id.age(graph, categories, agebins = 300,
    iterations = 5, norm = c(1, 1, 1))
measure.dynamics.lastcit(graph, agebins, iterations=5,
    norm.method="old", number=FALSE)
measure.dynamics.age(graph, agebins, iterations=5, norm.method="old",
    number=FALSE)
measure.dynamics.citedcat(graph, categories, iterations=5,
    number=FALSE, norm.method="old")
measure.dynamics.citingcat.citedcat(graph, categories, iterations=5,
    number=FALSE, norm.method="old", norm=c(1,1))

Arguments

graph The graph of which the evolution is quantified. It is assumed that the vertices were added in increasing order of vertex id.
agebins Numeric constant, the number of bins to use for measuring aging.
iterations Numeric constant, number of iterations to perform while calculating the attractiveness and the total attractiveness function.
time.window
vtime
etime
categories
norm
number
norm.method
error
cites
debug
debugdeg
which

Details

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

Value

TODO

Author(s)

Gabor Csardi csardi@rmki.kfki.hu


[Package igraph version 0.5.2 Index]