Network-Clustering/Schrick-Noah_CS-7863_Homework-3.R
2022-03-21 19:12:24 -05:00

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R

# Homework 3 for the University of Tulsa' s CS-7863 Network Theory Course
# Network Clustering
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
# Imports
library(igraph)
library(igraphdata)
library(WGCNA)
data(karate)
data(yeast)
g1 <- karate
g1.netname <- "Karate"
g2 <- yeast
g2.netname <- "Yeast"
##################### Part 1: Laplace Spectral Clustering #####################
########################## Part 2: Newman Modularity ##########################
##################### Part 3: Recursive Newman Modularity #####################
# Using igraph
karate.modularity <- fastgreedy.community(karate,merges=TRUE, modularity=TRUE, membership=TRUE)
#memberships <-community.to.membership(karate, karate.modularity$merges,
# steps=which.max(fgreedy$modularity)-1)
karate.modularity$membership
karate.modularity$merges
membership.ids <- unique(karate.modularity$membership)
membership.ids
cat(paste('Number of detected communities =',length(membership.ids)))
cat("community sizes: ")
sapply(membership.ids,function(x) {sum(x==karate.modularity$membership)})
cat("modularity: ")
max(karate.modularity$modularity)
#karate.modularity$modularity
V(karate)$color[karate.modularity$membership==1] <- "green"
V(karate)$color[karate.modularity$membership==2] <- "red"
V(karate)$color[karate.modularity$membership==3] <- "blue"
plot(karate,vertex.size=10,vertex.label=V(karate)$label,vertex.color=V(karate)$color)
###################### Part 4: TOM and Dynamic Tree Cut ######################
################################ Part 5: UMAP ################################