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

Description Usage Arguments Details Value Author(s) References Examples

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

1 2	ergmclust.plot(adjmat, K, directed = FALSE, thresh = 1e-06, iter.max = 200, coef.init = NULL, node.labels = NULL)

`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`.

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.

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

Authors: Amal Agarwal [aut, cre], Kevin Lee [aut], Lingzhou Xue [aut, cre], Anna Yinqi Zhang [cre]

Maintainer: Amal Agarwal <amalag.19@gmail.com>

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

## 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)