generate: generate adjacency matrix of stochastic blockmodel,...

In FusedPCA: Community Detection via Fused Principal Component Analysis

Description

To generate an adjacency matrix of stochastic blockmodel, degree-corrected block model or cockroach graph model.

Usage

gen.sbm(n, theta.in, theta.bw, K, seed)
gen.dcbm(n, theta.in, theta.bw, theta, K, seed)
gen.cr(n1)

Arguments

n1	input integer – one quarter of the number of nodes in the graph.
n	input integer – the number of nodes in EACH community.
theta.in	input real number, which is the probability of a within community edge.
theta.bw	input real number, which is the probability of a between community edge.
theta	input vector, of dimension number of nodes in ALL communities, with each entry equal to the individual effect of each node.
K	input integer – the number of communities.
seed	input integer – the random seed you can set.

Value

an adjacency matrix.

Author(s)

Yang Feng, Richard J. Samworth and Yi Yu

References

Yang Feng, Richard J. Samworth and Yi Yu, Community Detection via Fused Principal Component Analysis, manuscript. Holland, P.W., Laskey, K.B. and Leinhardt, S., 1983. Stochastic block models: first steps. Social Networks 5, 109-137. Karrer, B. and Newman, M.E.J., 2011. Stochastic blockmodels and community structure in networks. Physical Review E 83, 016107.

Examples

A1 = gen.sbm(n = 10, theta.in = 0.3, theta.bw = 0.1, K = 2, seed = 2)
A2 = gen.dcbm(n = 10, theta.in = 0.3, theta.bw = 0.1, 
theta = seq(from = 0.1, to = 0.5, length.out = 20), K = 2, seed = 2)
A3 = gen.cr(n1 = 10)

FusedPCA documentation built on May 29, 2017, 9:19 p.m.