# 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 ################################