Package 'netClust'

Model-Based Clustering of Network Data

Description

Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of netClust.

Details

The DESCRIPTION file:

Package:	netClust
Type:	Package
Title:	Model-Based Clustering of Network Data
Version:	1.0.2
Authors@R:	c(person(given = "Shuchismita", family = "Sarkar", role = c("aut", "cre"), email = "[email protected]"), person(given = "Volodymyr", family = "Melnykov", role = "aut"))
Maintainer:	Shuchismita Sarkar <[email protected]>
Description:	Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of netClust.
License:	GPL (>= 2)
Depends:	R (>= 2.10)
Imports:	Rcpp (>= 1.0.2)
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	yes
Packaged:	2026-01-23 15:15:46 UTC; shuch
Author:	Shuchismita Sarkar [aut, cre], Volodymyr Melnykov [aut]
Repository:	https://cran.r-universe.dev
Date/Publication:	2026-01-23 19:50:07 UTC
RemoteUrl:	https://github.com/cran/netClust
RemoteRef:	HEAD
RemoteSha:	5523b77795c994198c14ee7204984263ccb04886

Index of help topics:

netClust-package        Model-Based Clustering of Network Data
netData                 Dataset: netData
netDataID               Dataset: netDataID
netEM_multilayer        Returns the EM object for multilayer network
netEM_unilayer          Returns the EM object for unilayer network
rcpparma_hello_world    Set of functions in example RcppArmadillo
                        package

Provides model-based clustering for weighted networks. Core functions: netEM_unilayer() and netEM_multilayer().

Author(s)

Shuchismita Sarkar [aut, cre], Volodymyr Melnykov [aut]

Maintainer: Shuchismita Sarkar <[email protected]>

References

Melnykov, V., Sarkar, S. and Melnykov, Y., 2021. On finite mixture modeling and model-based clustering of directed weighted multilayer networks. Pattern Recognition, 112, p.107641.

Examples

data(netData) ## Read network data 
data(netDataID) ## Read original ID for network data

n <- dim(netData)[1] ## number of nodes of the network
p <- dim(netData)[4] ## number of layers of the network
K <- 2               ## number of clusters 
y <- netData

eps=0.0001
RndStrtUni= 5
RndStrtMult= 10
SmEMUni= 3
SmEMMult= 5
ItrSmEM=5
burn = 10*n
ItrMCMC= 100*n
sSigma = 1
sPsi = 1
a=0



#########################################################
### Run unilayer network EM seperately for each layer ###
#########################################################

for (MatC in 1:p){
  
  x <- array(0, dim = c(n,n,2))
  for (i in 1:n){
    for (j in 1:n){
      x[i,j,] <- y[i,j,,MatC]
    }
  }
  
  E <- netEM_unilayer(x, K, eps, RndStrtUni, SmEMUni, ItrSmEM, burn, ItrMCMC, sSigma,a)
  cat("Unilayer network", "Original ID", netDataID, "\n")
  cat("Unilayer network", "Feature", MatC, "Assigned ID", E$id, "\n")
  
}

##################################
### Run multilayer network EM  ###
##################################

E <- netEM_multilayer(y,K,p, eps, RndStrtMult, SmEMMult, ItrSmEM, burn, ItrMCMC, sSigma, sPsi, n, a)
cat("Multilayer network", "Original ID", netDataID, "\n")
cat("Multilayer network", "Assigned ID", E$id, "\n")

---

Dataset: netData

Description

Network data with 20 nodes and 2 layers

Usage

data("netData")

Format

The format is: num [1:20, 1:20, 1:2, 1:2] 0 0 0 0 0 0 0 0 0 0 ...

Details

Dataset demonstrating multilayer network

Source

Sarkar, S. (2020)

References

Sarkar, S. (2019) On the use of transformations for modeling multidimensional heterogeneous data, The University of Alabama Libraries Digital Collections

Examples

data(netData)
## maybe str(netData) ; plot(netData) ...

---

Dataset: netDataID

Description

ID for netData dataset

Usage

data("netDataID")

Format

A data frame with 30 observations on the following 1 variable.

netDataID

a numeric vector

Details

ID for the dataset demonstrating multilayer network

Source

Sarkar, S. (2020)

References

Sarkar, S. (2019) On the use of transformations for modeling multidimensional heterogeneous data, The University of Alabama Libraries Digital Collections

Examples

data(netDataID)
## maybe str(netDataID) ; plot(netDataID) ...

---

Returns the EM object for multilayer network

Description

Returns the EM object for multilayer network

Usage

netEM_multilayer(
  y,
  K,
  p,
  eps,
  num_rand_start,
  num_run_smallEM,
  max_itr_smallEM,
  burn,
  MCMC_itr,
  sigma_mult,
  psi_mult,
  n,
  alpha
)

Arguments

y	multiple network
K	number of clusters
p	number of layers
eps	epsilon for convergence
num_rand_start	number of random starts
num_run_smallEM	number of runs for small EM
max_itr_smallEM	maximum number of runs for small EM
burn	number of runs for burn for Metropolis Hastings
MCMC_itr	number of runs for Metropolis Hastings iterations
sigma_mult	scaling multiplier for Sigma matrix
psi_mult	scaling multiplier for Psi matrix
n	number of nodes of the network
alpha	seed provided by the user

Value

EM object

---

Returns the EM object for unilayer network

Description

Returns the EM object for unilayer network

Usage

netEM_unilayer(
  x,
  K,
  eps,
  num_rand_start,
  num_run_smallEM,
  max_itr_smallEM,
  burn,
  MCMC_itr,
  sigma_mult,
  alpha
)

Arguments

x	multiple network
K	number of clusters
eps	epsilon for convergence
num_rand_start	number of random starts
num_run_smallEM	number of runs for small EM
max_itr_smallEM	maximum number of runs for small EM
burn	number of runs for burn for Metropolis Hastings
MCMC_itr	number of runs for Metropolis Hastings iterations
sigma_mult	scaling multiplier for Sigma matrix
alpha	seed provided by the user

Value

EM object

---

Set of functions in example RcppArmadillo package

Description

These four functions are created when RcppArmadillo.package.skeleton() is invoked to create a skeleton packages.

Usage

rcpparma_hello_world()
rcpparma_outerproduct(x)
rcpparma_innerproduct(x)
rcpparma_bothproducts(x)

Arguments

x	a numeric vector

Details

These are example functions which should be largely self-explanatory. Their main benefit is to demonstrate how to write a function using the Armadillo C++ classes, and to have to such a function accessible from R.

Value

rcpparma_hello_world() does not return a value, but displays a message to the console.

rcpparma_outerproduct() returns a numeric matrix computed as the outer (vector) product of x.

rcpparma_innerproduct() returns a double computer as the inner (vector) product of x.

rcpparma_bothproducts() returns a list with both the outer and inner products.

Author(s)

Dirk Eddelbuettel

References

See the documentation for Armadillo, and RcppArmadillo, for more details.

Examples

x <- sqrt(1:4)
  rcpparma_innerproduct(x)
  rcpparma_outerproduct(x)

---