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.

TODO

### Author(s)

Gabor Csardi csardi@rmki.kfki.hu

[Package igraph version 0.5 Index]