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can you create the code for R that runs the louvain algorithm on a network of 2 0 nodes and is undirected where it visualises

can you create the code for R that runs the louvain algorithm on a network of 20 nodes and is undirected where it visualises/plots each step( modularity optimization and community aggregation). I have used the following code and works apart from it doesnt have edges between the nodes in the community aggregation graph: # Load necessary library
library(igraph)
# Define the edges of your network (adjust as needed)
edges <- c(
1,2,1,3,1,4,1,5,
2,3,2,4,2,5,2,6,
3,4,3,5,3,6,3,7,
4,5,4,6,4,7,4,8,
5,6,5,7,5,8,5,9,
6,7,6,8,6,9,6,10,
7,8,7,9,7,10,7,11,
8,9,8,10,8,11,8,12,
9,10,9,11,9,12,9,13,
10,11,10,12,10,13,10,14,
11,12,11,13,11,14,11,15,
12,13,12,14,12,15,12,16,
13,14,13,15,13,16,13,17,
14,15,14,16,14,17,14,18,
15,16,15,17,15,18,15,19,
16,17,16,18,16,19,16,20,
17,18,17,19,17,20,
18,19,18,20,
19,20
)
# Create the initial graph (ensure it's undirected)
graph <- graph(edges, directed = FALSE)
# Initial plot
plot(graph, main = "Original Network")
# Step 1: Modularity Optimization
louvain_partition <- cluster_louvain(graph)
# Visualize the modularity optimization
plot(louvain_partition, graph, main = "Modularity Optimization")
# Step 2: Community Aggregation
while (TRUE){
previous_partition <- louvain_partition
# Create a new graph with each unique community as a single node
unique_communities <- unique(membership(louvain_partition))
aggregated_graph <- make_empty_graph(n = length(unique_communities), directed = FALSE)
V(aggregated_graph)$name <- as.character(unique_communities)
# Iterate through original edges and add edges between communities
for (i in 1:(length(edges)/2)){
from_node <- edges[2* i -1]
to_node <- edges[2* i]
from_comm <- membership(louvain_partition)[from_node]
to_comm <- membership(louvain_partition)[to_node]
if (!is.na(from_comm) && !is.na(to_comm) && from_comm != to_comm){
from_vertex <- which(V(aggregated_graph)$name == as.character(from_comm))
to_vertex <- which(V(aggregated_graph)$name == as.character(to_comm))
add_edges(aggregated_graph, c(from_vertex, to_vertex))
}
}
# Re-run modularity optimization on aggregated graph
louvain_partition <- cluster_louvain(aggregated_graph)
# Visualize the community aggregation
plot(louvain_partition, aggregated_graph, main = "Community Aggregation")
# Check for convergence
if (identical(membership(previous_partition), membership(louvain_partition))){
break
}
}

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