cluster_k_component_graph: Cluster a k-component graph from data using the Constrained...

In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

Cluster a k-component graph from data using the Constrained Laplacian Rank algorithm Cluster a k-component graph on the basis of an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.

Description

Cluster a k-component graph from data using the Constrained Laplacian Rank algorithm

Cluster a k-component graph on the basis of an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.

Usage

cluster_k_component_graph(
  Y,
  k = 1,
  m = 5,
  lmd = 1,
  eigtol = 1e-09,
  edgetol = 1e-06,
  maxiter = 1000
)

Arguments

Y	a pxn data matrix, where p is the number of nodes and n is the number of features (or data points per node)
k	the number of components of the graph
m	the maximum number of possible connections for a given node used to build an affinity matrix
lmd	L2-norm regularization hyperparameter
eigtol	value below which eigenvalues are considered to be zero
edgetol	value below which edge weights are considered to be zero
maxiter	the maximum number of iterations

Value

A list containing the following elements:

laplacian	the estimated Laplacian Matrix
adjacency	the estimated Adjacency Matrix
eigvals	the eigenvalues of the Laplacian Matrix
lmd_seq	sequence of lmd values at every iteration
elapsed_time	elapsed time at every iteration

Author(s)

Ze Vinicius and Daniel Palomar

References

Nie, Feiping and Wang, Xiaoqian and Jordan, Michael I. and Huang, Heng. The Constrained Laplacian Rank Algorithm for Graph-based Clustering, 2016, AAAI'16. http://dl.acm.org/citation.cfm?id=3016100.3016174

Examples

library(clusterSim)
library(spectralGraphTopology)
library(igraph)
set.seed(1)
# number of nodes per cluster
N <- 30
# generate datapoints
twomoon <- shapes.two.moon(N)
# estimate underlying graph
graph <- cluster_k_component_graph(twomoon$data, k = 2)
# build network
net <- graph_from_adjacency_matrix(graph$adjacency, mode = "undirected", weighted = TRUE)
# colorify nodes and edges
colors <- c("#706FD3", "#FF5252", "#33D9B2")
V(net)$cluster <- twomoon$clusters
E(net)$color <- apply(as.data.frame(get.edgelist(net)), 1,
                      function(x) ifelse(V(net)$cluster[x[1]] == V(net)$cluster[x[2]],
                                        colors[V(net)$cluster[x[1]]], '#000000'))
V(net)$color <- c(colors[1], colors[2])[twomoon$clusters]
# plot network
plot(net, layout = twomoon$data, vertex.label = NA, vertex.size = 3)

spectralGraphTopology documentation built on March 18, 2022, 7:35 p.m.