gen_rand_conn

Creates a randomly permuted DCPP connectivity matrix with a given average
expected degree

models

Features tools for the network data analysis and community detection.
Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models.
Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models.
Implements the following:
Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138>
Bickel and Sarkar (2015) <doi:10.1111/rssb.12117>
Lei (2016) <doi:10.1214/15-AOS1370>
Wang and Bickel (2017) <doi:10.1214/16-AOS1457>
Zhang and Amini (2020) <arXiv:2012.15047>
Le and Levina (2022) <doi:10.1214/21-EJS1971>.

Arash A. Amini

nett

Network Analysis and Community Detection

Linfan Zhang

gen_rand_conn function

<dl><dt>n</dt>
<dd>number of nodes</dd>
<dt>K</dt>
<dd>number of communities</dd>
<dt>lambda</dt>
<dd>expected average degree</dd>
<dt>gamma</dt>
<dd>a measure of out-in-ratio (convex combination parameter)</dd>
<dt>pri</dt>
<dd>the prior on community labels</dd>
<dt>theta</dt>
<dd>node connection propensity parameter of DCSBM, by default
E(theta) = 1</dd></dl>

Arguments

Generate randomly permuted connectivity matrix — gen_rand_conn

<dl>

<dt>n</dt>
<dd>number of nodes</dd>


<dt>K</dt>
<dd>number of communities</dd>


<dt>lambda</dt>
<dd>expected average degree</dd>


<dt>gamma</dt>
<dd>a measure of out-in-ratio (convex combination parameter)</dd>


<dt>pri</dt>
<dd>the prior on community labels</dd>


<dt>theta</dt>
<dd>node connection propensity parameter of DCSBM, by default
E(theta) = 1</dd>

</dl>

gen_rand_conn: Generate randomly permuted connectivity matrix

Description

Usage

Value

Arguments

Details