ergmclust (version 1.0.0)

ergmclust.plot: Visualization For Model-Based Clustering of Large Networks.

Description

Visualization of the network data with the clusters node colors representing different clusters in the Exponential-Family Random Graph Models (ERGMs) clustered network.

Usage

ergmclust.plot(adjmat, K, directed = FALSE, thresh = 1e-06, 
iter.max = 200, coef.init = NULL, node.labels = NULL)

Arguments

adjmat

An object of class matrix of dimension (N x N) containing the adjacency matrix, where N is the number of nodes in the network.

K

Number of clusters in the mixed membership Exponential-Family Random Graph Models (ERGMs).

directed

If TRUE, the network is supposed to be directed (and therefore adjmat is must be asymmetric in general). By default, this is set as FALSE.

thresh

Optional user-supplied convergence threshold for relative error in the objective in Variational Expectation-Maximization (VEM) algorithm. The default value is set as 1e-06.

iter.max

The maximum number of iterations after which the algorithm is terminated. The default value is set as 200.

coef.init

Optional user-supplied network canonical parameter vector (K-dimensional); default is NULL, and ergmclust chooses a random perturbation around zero vector.

node.labels

Optional user-supplied network node names character vector (N-dimensional); default is NULL.

Value

Returns a plot of network object with colored nodes corresponding to K clusters.

Details

ergmclust.plot provides the visualization tool for network data clustered through mixed membership Exponential-Family Random Graph Models (ERGMs). The optional argument node.labels could help track the cluster membership of specific nodes.

References

Vu D. Q., Hunter, D. R., and Schweinberger, M. (2013) Model-based Clustering of Large Networks, The Annals of Applied Statistics, Vol. 7(2), 1010-1039

https://projecteuclid.org/euclid.aoas/1372338477

Examples

# NOT RUN {
## undirected network:
data(tradenet)
## Plotting clustered network
ergmclust.plot(adjmat = tradenet, K = 2, directed = FALSE, 
thresh = 1e-06)

## directed network:
data(armsnet)
## Plotting clustered network
ergmclust.plot(adjmat = armsnet, K = 2, directed = TRUE, 
thresh = 1e-06)
# }