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 |
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 |
node.labels |
Optional user-supplied network node names character vector (N-dimensional); default is |
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
1 2 3 4 5 6 7 8 9 10 11 | ## 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)
|
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