Package {scalednap}

scalednap

Description

Given a non-oriented graph, calculates the optimal vertex partition using persistence as the objective function.

Details

See manual entries.

Author(s)

Maintainer: Alessandro Avellone alessandro.avellone@unimib.it

Authors:

• Alessandro Avellone alessandro.avellone@unimib.it

• Paolo Bartesaghi paolo.bartesaghi@unimi.it

• Stefano Benati stefano.benati@unitn.it

• Rosanna Grassi rosanna.grassi@unimib.it

cluster_milano

Description

Calculates the vertex partition with maximum global null-adjusted persistence. This function is polymorphic: it automatically detects the input type and accepts either a vertex vector (accompanied by an edge list) or directly an igraph object.

Usage

cluster_milano(x, ...)

## Default S3 method:
cluster_milano(
  x,
  edge_list,
  weights = NULL,
  membership = NULL,
  H0 = 0,
  seed = NULL,
  n_restarts = 1L,
  tol = NULL,
  max_level = 0L,
  num_threads = 1L,
  collect_stats = FALSE,
  ...
)

## S3 method for class 'igraph'
cluster_milano(
  x,
  membership = NULL,
  H0 = 0,
  seed = NULL,
  n_restarts = 1L,
  tol = NULL,
  max_level = 0L,
  num_threads = 1L,
  collect_stats = FALSE,
  ...
)

Arguments

Value

A list with five elements:

membership

The optimal vertex partition.

score

The measure value of the optimal partition.

clusters_value

Numeric vector with the score of each cluster, or NULL unless collect_stats = TRUE.

iterations

Integer vector: number of move-node sweeps performed at each coarsening level.

seed

The master seed actually used (numeric). Re-running with seed set to this value and the same n_restarts reproduces the result exactly. NA when the run was launched with an exact 64-bit seed string (no master exists in that case).

winning_restart

1-based index of the restart that produced the returned partition.

seed_winning

Character string: the exact 64-bit seed used by the winning restart (returned as a string because R doubles are exact only up to 2^53). Pass it back as seed to reproduce that single winning run directly.

Examples

library(scalednap)

# --- EXAMPLE 1: Standard input (vectors and matrices) ---
edg <- c(1, 2, 1, 3, 1, 4, 2, 3, 3, 4, 4, 5, 5, 6, 5, 7, 6, 7)
edge_list <- matrix(edg, ncol = 2, byrow = TRUE)
vertex <- c(1, 2, 3, 4, 5, 6, 7)
cluster_milano(x = vertex, edge_list = edge_list)

# --- EXAMPLE 2: igraph input ---
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_ring(10)
  cluster_milano(g)
}

global_persistence

Description

Given a partition of the graph vertices, calculates the global persistence as the sum of the local persistences of the individual clusters. Persistence can be either null-adjusted or probability-based. This function is polymorphic: it automatically detects the input type and accepts either a vertex vector (accompanied by an edge list) or directly an igraph object.

Usage

global_persistence(x, ...)

## Default S3 method:
global_persistence(x, edge_list, weights = NULL, membership, H0 = 0, ...)

## S3 method for class 'igraph'
global_persistence(x, membership, H0 = 0, ...)

Arguments

Value

A list with two elements:

score

The global persistence of the partition.

clusters_value

The local persistence of each cluster. A value of NaN indicates that cluster C_k is empty in the input membership.

Examples

library(scalednap)

# --- EXAMPLE 1: Standard input (vectors and matrices) ---
edg <- c(1, 2, 1, 3, 1, 4, 2, 3, 3, 4, 4, 5, 5, 6, 5, 7, 6, 7)
edge_list <- matrix(edg, ncol = 2, byrow = TRUE)
vertex <- c(1, 2, 3, 4, 5, 6, 7)
mem <- c(1, 1, 1, 1, 2, 2, 2)
global_persistence(x = vertex, edge_list = edge_list, membership = mem)

# --- EXAMPLE 2: igraph input ---
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_ring(10)
  mem <- c(rep(1, 5), rep(2, 5))
  global_persistence(g, membership = mem)
}

local_persistence

Description

Given the incidence vector of a vertex subset, calculates either the persistence probability or the null-adjusted persistence of cluster C. This function is polymorphic: it automatically detects the input type and accepts either a vertex vector (accompanied by an edge list) or directly an igraph object.

Usage

local_persistence(x, ...)

## Default S3 method:
local_persistence(x, edge_list, weights = NULL, cluster, H0 = 0, ...)

## S3 method for class 'igraph'
local_persistence(x, cluster, H0 = 0, ...)

Arguments

Value

Numeric scalar: the persistence probability when H0 = NULL, the null-adjusted persistence (NAP) when H0 = 0, or the scaled null-adjusted persistence (Scaled-NAP, sNAP_{\alpha}, with \alpha = H0) when H0 is in (0, 1].

Examples

library(scalednap)

# --- EXAMPLE 1: Standard input (vectors and matrices) ---
edg <- c(1, 2, 1, 3, 1, 4, 2, 3, 3, 4, 4, 5, 5, 6, 5, 7, 6, 7)
edge_list <- matrix(edg, ncol = 2, byrow = TRUE)
vertex <- c(1, 2, 3, 4, 5, 6, 7)
cluster_bin <- c(1, 1, 1, 1, 0, 0, 0)
local_persistence(x = vertex, edge_list = edge_list, cluster = cluster_bin)

# --- EXAMPLE 2: igraph input ---
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_ring(10)
  cluster_bin <- c(rep(1, 5), rep(0, 5))
  local_persistence(g, cluster = cluster_bin)
}