create a community matrix, then make a cluster graph based on eigenvalues and eigenvectors in r

install.packages("igraph")
library(igraph)
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# Create a sample adjacency matrix (replace this with your own data)
adj_matrix <- matrix(sample(c(0, 1), 25, replace = TRUE), nrow = 5)
diag(adj_matrix) <- 0  # Set diagonal elements to 0 for a non-self-loop graph

# Create an igraph graph object from the adjacency matrix
graph <- graph_from_adjacency_matrix(adj_matrix, mode = "undirected")

# Create a community matrix (partition your graph into communities)
community_matrix <- matrix(sample(1:3, vcount(graph), replace = TRUE), ncol = 1)

# Assign the community membership to the vertex attribute 'membership'
V(graph)$membership <- community_matrix

# Plot the graph
plot(graph)
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# Compute the eigenvalues and eigenvectors of the community matrix
eigen_data <- eigen(community_matrix)
# Extract eigenvalues
eigenvalues <- eigen_data$values
# Extract eigenvectors
eigenvectors <- eigen_data$vectors

# Create a cluster graph based on eigenvectors
clust_graph <- graph.adjacency(eigenvectors > 0, mode = "undirected", diag = FALSE)

# Plot the cluster graph
plot(clust_graph)
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