gmmase: Run spectral clustering on a (possibly directed) (possibly...

In youngser/gmmase: Graph Spectral Clustering

Description

It does 1. do a pass-to-rank for a weighted graph (PTR, no-op for an unweighted graph), 2. do a graph spectral embedding (ASE or LSE) with a _diagonal augmentation_, 3. do a _dimension reduction_ (ZG) and merge left and right vectors (no-op for an undirected graph), 4. cluster vertices (GMM or Kmeans).

Usage

gmmase(g, dmax = 2, elb = 1, abs = FALSE, lcc = TRUE, embed = "ASE",
  clustering = "GMM", Kmax = 9, weight = "raw", verbose = TRUE,
  doplot = FALSE)

Arguments

g	a graph in igraph format
dmax	maximum dimension for embedding
elb	an index for elbow
abs	a boolean to take abs on elbow finder or not
embed	either ASE or LSE, spectral embedding method
clustering	either GMM or Kmeans, clustering method
Kmax	maximum number of clusters
weight	either ptr pr binary or raw to determine whether to perform pass-to-rank or not, default is raw
verbose	boolean to determine whether to display an intermediate fitting progress status of mclust or not, default is TRUE
doplot	boolean to determine whether to draw plots or not, default is TRUE

Value

g the largest connected component of the input graph

mc clustering output object

Y labels for the clustering

Author(s)

Youngser Park <[e-mail]>

References

D.L. Sussman, M. Tang, D.E. Fishkind, and C.E. Priebe, A consistent adjacency spectral embedding for stochastic blockmodel graphs, Journal of the American Statistical Association, Vol. 107, No. 499, pp. 1119-1128, 2012.

Examples

library(igraph)
data(g)
E(g)$weight <- runif(ecount(g), 1, 5) # add random edge weights
Y <- gmmase(g, dmax=20, use.ptr=TRUE, embed="ASE", clustering="Kmeans")

youngser/gmmase documentation built on May 30, 2019, 7:19 p.m.