Load library

library(igraph)

STEP 1: Load Facebook dataset

edges <- read.table(“facebook_combined.txt”, header = FALSE) g_fb <- graph_from_edgelist(as.matrix(edges), directed = FALSE)

Basic info about Facebook network

cat(“Facebook Network:”) cat(“Nodes:”, vcount(g_fb), “”) cat(“Edges:”, ecount(g_fb), “”) cat(“Density:”, edge_density(g_fb), “”)

STEP 2: Create Random Graph (same nodes & edges)

n_nodes <- vcount(g_fb) n_edges <- ecount(g_fb)

g_random <- sample_gnm(n = n_nodes, m = n_edges, directed = FALSE)

Basic info about Random network

cat(“Random Network:”) cat(“Nodes:”, vcount(g_random), “”) cat(“Edges:”, ecount(g_random), “”) cat(“Density:”, edge_density(g_random), “”)

STEP 3: Compare Clustering

cat(“Clustering Coefficient:”) cat(“Facebook:”, transitivity(g_fb, type=“global”), “”) cat(“Random:”, transitivity(g_random, type=“global”), “”)

STEP 4: Compare Average Path Length

cat(“Average Path Length:”) cat(“Facebook:”, mean_distance(g_fb), “”) cat(“Random:”, mean_distance(g_random), “”)

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