Description Usage Arguments Value References Examples

Given a list of graphs, 'graph.cluster' builds a hierarchy of clusters according to the Jensen-Shannon divergence between graphs.

1 | ```
graph.cluster(x, k, method = "complete", bandwidth = "Silverman")
``` |

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

`k` |
the number of clusters. |

`method` |
the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of ‘"ward.D"’, ‘"ward.D2"’, ‘"single"’, ‘"complete"’, ‘"average"’ (= UPGMA), ‘"mcquitty"’ (= WPGMA), ‘"median"’ (= WPGMC) or ‘"centroid"’ (= UPGMC). |

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

A list containing:

`hclust` |
an object of class *hclust* which describes the tree produced by the clustering process. |

`cluster` |
the clustering labels for each graph. |

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.cluster(g, 3)
``` |

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