Description Usage Arguments Value References Examples

'graph.mult.scaling' performs multidimensional scaling of graphs. It takes the Jensen-Shannon divergence between graphs (JS) and uses the 'cmdscale' function from the 'stats' package to obtain a set of points such that the distances between the points are similar to JS.

1 2 | ```
graph.mult.scaling(x, plot = TRUE, bandwidth = "Silverman", type = "n",
main = "", ...)
``` |

`x` |
a list of adjacency matrices. For unweighted graphs, each matrix contains only 0s and 1s. For weighted graphs, each matrix may contain nonnegative real values that correspond to the weights of the edges. |

`plot` |
logical. If TRUE (default) the points chosen to represent the Jensen-Shannon divergence between graphs are plotted. |

`bandwidth` |
character string indicating which criterion will be used to choose the bandwidth for the spectral density estimation. The available criteria are "Silverman" (default) and "Sturges". |

`type` |
what type of plot should be drawn. The defaut value is '"n"', which indicates that the points will not be plotted (i. e. only the labels of the graphs will be plotted). |

`main` |
title of the plot (default value is ""). |

`...` |
additional plotting parameters. See 'plot' function from the 'graphics' package for the complete list. |

A matrix in which each column corresponds to a coordinate and each row corresponds to a graph (point). Then, each row gives the coordinates of the points chosen to represent the Jensen-Shannon divergence between graphs.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
require(igraph)
g <- list()
for (i in 1:5) {
g[[i]] <- as.matrix(get.adjacency(
erdos.renyi.game(50, 0.5, type="gnp",
directed = FALSE)))
}
for (i in 6:10) {
g[[i]] <- as.matrix(get.adjacency(
watts.strogatz.game(1, 50, 8, 0.2)))
}
for (i in 11:15) {
g[[i]] <- as.matrix(get.adjacency(
barabasi.game(50, power = 1,
directed = FALSE)))
}
graph.mult.scaling(g)
``` |

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