In colab, change the runtime to use R. Drag and drop your data into the file tray. Then use the code below to get you started. Do not just dump this all into a single block. Try to maintain one block = one main operation. And then use text blocks between the code blocks to keep track of your thoughts etc.

# install igraph; this might take a long time
# you only run this line the first time you install igraph:
install.packages('igraph')
# a lot of stuff gets downloaded and installed.
# 
# now tell RStudio you want to use the igraph pacakge and its functions:
library('igraph')

# now let's load up the data by putting the csv files into nodes and links.
# we're keeping the first row as a 'header'

nodes <- read.csv("nodes.csv", header=T, as.is=T)
links <- read.csv("edges.csv", header=T, as.is=T)
#examine data
head(nodes)
head(links)

#we are going to tell igraph that the network is directed, that the relationship Alice to Bob is different than Bob's to Alice. This isn't always a critical distinction to make and depends on your dataset.
#AND - we're going to do this just from the edge data
#Create network from edges only - igraph will infer the nodes
net <- graph_from_data_frame(d=links, directed=T)

#(if we wanted to include the node data specifically, we could do this:
# net <- graph_from_data_frame(d=links, vertices=nodes, directed=T)
# see the difference?
# Calculate closeness centrality
closeness_cent <- closeness(net, normalized = TRUE)

# Histogram
hist(closeness_cent, 
     breaks = 20,
     main = "Distribution of Closeness Centrality",
     xlab = "Closeness Centrality",
     ylab = "Frequency",
     col = "lightgreen",
     border = "white")
abline(v = mean(closeness_cent), col = "red", lwd = 2, lty = 2)

# Network plot colored by closeness
close_colors <- colorRampPalette(c("lightblue", "darkgreen"))(100)
V(net)$color <- close_colors[as.numeric(cut(closeness_cent, breaks = 100))]

plot(net, 
     layout = layout_with_fr,
     vertex.size = closeness_cent * 50 + 5,  # Scale by closeness
     vertex.color = V(net)$color,
     vertex.frame.color = "white",
     edge.color = "gray50",
     edge.arrow.size = 0.5,
     vertex.label = NA,
     main = "Network: Closeness Centrality")

# Calculate betweenness centrality
betweenness_cent <- betweenness(net, normalized = TRUE)

# Histogram
hist(betweenness_cent, 
     breaks = 20,
     main = "Distribution of Betweenness Centrality",
     xlab = "Betweenness Centrality",
     ylab = "Frequency",
     col = "orange",
     border = "white")
abline(v = mean(betweenness_cent), col = "red", lwd = 2, lty = 2)

# Network plot colored by betweenness
between_colors <- colorRampPalette(c("lightblue", "darkorange"))(100)
V(net)$color <- between_colors[as.numeric(cut(betweenness_cent, breaks = 100))]

plot(net, 
     layout = layout_with_fr,
     vertex.size = sqrt(betweenness_cent) * 10 + 5,  # Square root scaling
     vertex.color = V(net)$color,
     vertex.frame.color = "white",
     edge.color = "gray50",
     edge.arrow.size = 0.5,
     vertex.label = NA,
     main = "Network: Betweenness Centrality")
# Detect communities using modularity
communities <- cluster_louvain(as.undirected(net))  # Convert to undirected for community detection
modularity_score <- modularity(communities)

# Print modularity score
cat("Modularity score:", modularity_score, "\n")
cat("Number of communities:", length(communities), "\n")

# Histogram of community sizes
community_sizes <- sizes(communities)
hist(community_sizes, 
     breaks = 10,
     main = paste("Distribution of Community Sizes\nModularity =", round(modularity_score, 3)),
     xlab = "Community Size",
     ylab = "Frequency",
     col = "purple",
     border = "white")

# Network plot colored by community
community_colors <- rainbow(length(communities))
V(net)$color <- community_colors[membership(communities)]

plot(net, 
     layout = layout_with_fr,
     vertex.size = 8,
     vertex.color = V(net)$color,
     vertex.frame.color = "white",
     edge.color = "gray50",
     edge.arrow.size = 0.5,
     vertex.label = NA,
     main = paste("Network: Communities (Modularity =", round(modularity_score, 3), ")"))