| BTSBM {HCD} | R Documentation |
Generates networks from binary tree stochastic block model
Description
Generates networks from binary tree stochastic block model, with provided sequence of connection probability along the tree
Usage
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 <tianxili@umn.edu>
References
Li, T., Lei, L., Bhattacharyya, S., Van den Berge, K., Sarkar, P., Bickel, P.J. and Levina, E., 2022. Hierarchical community detection by recursive partitioning. Journal of the American Statistical Association, 117(538), pp.951-968.
Examples
dt <- BTSBM(n=1600,d=4,a.seq=0.2^seq(0,4),lambda=50)
A <- dt$A.list[[1]]