rmultsbm: Generate a graph adjacency matrix using a Stochastic Block...

In comeetie/greed: Clustering and Model Selection with the Integrated Classification Likelihood

Generate a graph adjacency matrix using a Stochastic Block Model

Description

rmultsbm returns the multi-graph adjacency matrix and the cluster labels generated randomly with a Multinomial Stochastic Block Model.

Usage

rmultsbm(N, pi, mu, lambda)

Arguments

N	The size of the graph to generate
pi	A numeric vector of length K with clusters proportions (will be normalized to sum up to 1).
mu	A numeric array of dim K x K x M with the connectivity pattern to generate. elements in [0,1].
lambda	A double with the Poisson intensity to generate the total counts

Details

This function takes the desired graph size, cluster proportions and connectivity matrix as input and sample a graph accordingly together with the clusters labels.

Value

A list with fields:

• x: the multi-graph adjacency matrix as an array

• K: number of generated clusters

• N: number of vertex

• cl: vector of clusters labels

• pi: clusters proportions

• mu: connectivity matrix

• lambda:

Examples

simu <- rsbm(100, rep(1 / 5, 5), diag(rep(0.1, 5)) + 0.001)

comeetie/greed documentation built on Oct. 10, 2022, 5:37 p.m.