# measure.dynamics

From igraph v0.5.2-2
by Gabor Csardi

##### Measuring the driving force in evolving networks

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.

- Keywords
- graphs

##### 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

*Documentation reproduced from package igraph, version 0.5.2-2, License: GPL (>= 2)*

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