BTSBM: Generates networks from binary tree stochastic block model

Description Usage Arguments Value Author(s) References Examples

View source: R/HCD-Implement.R

Description

Generates networks from binary tree stochastic block model, with provided sequence of connection probability along the tree

Usage

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BTSBM(n, d, a.seq, lambda, alpha = NULL, N = 1)

Arguments

n

number of nodes in the network

d

number of layers until leaves (excluding the root)

a.seq

the connection probability sequence along the tree, a_r, see details in the paper

lambda

average node degree, only used when alpha is not provided

alpha

the common scaling of the a_r sequence. So at the end, essentially the a_r sequence is a.seq*alpha

N

the number of networks to generate from the same model

Value

A list of objections of

A.list

the generated network adjacency matrices

B

the connection probability matrix between K communities, where K = 2^d

label

the vector of community labels for n nodes

P

the connection probability matrix between the n nodes. It is the expectation of adjacency matrices, except on the diagonal

comm.sim.mat

the binary string similarity matrix between communities

node.sim.mat

the binary string similarity matrix between nodes

Author(s)

Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina.

Maintainer: Tianxi Li <[email protected]>

References

Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina. Hierarchical community detection by recursive partitioning. arXiv:1810.01509

Examples

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dt <- BTSBM(n=1600,d=4,a.seq=0.2^seq(0,4),lambda=50)
A <- dt$A.list[[1]]

HCD documentation built on Aug. 24, 2019, 5:03 p.m.