# graph.entropy: Graph spectral entropy In statGraph: Statistical Methods for Graphs

## Description

'graph.entropy' returns the spectral entropy of a given undirected graph.

## Usage

 `1` ```graph.entropy(A = NULL, bandwidth = "Silverman", eigenvalues = NULL) ```

## Arguments

 `A` the adjacency matrix of the graph. For an unweighted graph it contains only 0s and 1s. For a weighted graph, it may contain nonnegative real values that correspond to the weights of the edges. `bandwidth` string indicating which criterion will be used to choose the bandwidth during the spectral density estimation. The available criteria are "Silverman" (default) and "Sturges". `eigenvalues` optional parameter. It contains the eigenvalues of matrix A. Then, if the eigenvalues of matrix A have already been computed, this parameter can be used instead of A. If no value is passed, then the eigenvalues of A will be computed by 'graph.entropy'.

## Value

a real number corresponding to the graph spectral entropy.

## References

Takahashi, D. Y., Sato, J. R., Ferreira, C. E. and Fujita A. (2012) Discriminating Different Classes of Biological Networks by Analyzing the Graph Spectra Distribution. _PLoS ONE_, *7*, e49949. doi:10.1371/journal.pone.0049949.

Silverman, B. W. (1986) _Density Estimation_. London: Chapman and Hall.

Sturges, H. A. The Choice of a Class Interval. _J. Am. Statist. Assoc._, *21*, 65-66.

## Examples

 ```1 2 3 4 5``` ```require(igraph) G <- erdos.renyi.game(100, p=0.5) A <- as.matrix(get.adjacency(G)) entropy <- graph.entropy(A) entropy ```

statGraph documentation built on May 29, 2017, 9:08 a.m.