cluster_milano: cluster Milano

In persistence: Optimal Graph Partition using the Persistence

cluster Milano

Description

Calculates the partition with maximum global null-adjuted persistence.

Usage

cluster_milano(vertex, edge_list, seed = NULL)

Arguments

vertex	the vertices of the graph, whose label are integers and they must be consistent with the edge sets.
edge_list	the graph edge list in the form of an integer matrix with two columns.
seed	As some steps of the algorithm are random, users may experiments with different seeds of random numbers.

Value

A list containing:

membeship

The optimal vertex partition.

value

The null-adjusted persistence of the partition.

seed

The used seed to generate random numbers.

Examples

library(persistence)
library(igraph)

edg = c(1, 2, 1, 3, 1, 4, 2, 3, 3, 4, 4, 5, 5, 6, 5, 7, 5, 8, 5, 9, 6, 7, 6, 8, 7, 9, 8, 9)
print(length(edg) / 2.0)
vertex = unique(edg)
edg = t(matrix(as.integer(edg), nrow = 2 ))
rete <- graph_from_edgelist(edg, directed = FALSE)
plot(rete)
seed <- sample(1:as.integer(.Machine$integer.max),1, replace= FALSE)
r = cluster_milano(vertex, edg, seed=seed)
print(paste("The optimal null-adjusted persistence is: ", r$measure))
print(paste("The optimal persistence probability is: ", r$measure + 1))

persistence documentation built on June 8, 2025, 12:26 p.m.