# measure.dynamics

From igraph v0.1.2
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, sd=FALSE,
estind=NULL, estage=NULL)
```

##### 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.
- sd
- Logical, should an estimation of the error also calculated.
- estind
- estage

##### Details

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

##### Value

- A named list with components
`akl`

(the attractiveness function),`st`

(total attractiveness versus time),`sd`

(only if the`sd`

arguments was`TRUE`

, the estimation of the error for the`akl`

values).

##### Examples

```
g <- barabasi.game(10000)
mes <- measure.dynamics.idage(g, 20, sd=TRUE)
```

*Documentation reproduced from package igraph, version 0.1.2, License: GPL version 2 or later (June, 1991)*

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