create a small community matrix, then use igraph to isolate each pathway that forms a loop into a dataframe in r

To create a small community matrix and isolate each pathway that forms a loop into a dataframe using igraph in R, you can follow these steps:

First, install and load the necessary packages:


main.r
install.packages(c("igraph", "data.table"))
library(igraph)
library(data.table)
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Next, create a small graph:


main.r
# Create a small graph
g <- make_ring(10)
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Now, let's find all simple cycles in the graph:


main.r
# Find all simple cycles in the graph
cycles <- all_simple_cycles(g, min = 3, max = length(V(g)))
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To isolate each pathway that forms a loop into a dataframe, you can use the following code:


main.r
# Create a list to store the cycles
cycle_list <- vector("list", length(cycles))

# Loop through the cycles
for (i in 1:length(cycles)) {
  # Convert the cycle to a dataframe
  cycle_df <- data.frame(path = unlist(cycles[[i]]))
  # Add the cycle to the list
  cycle_list[[i]] <- cycle_df
}

# Convert the list to a single dataframe
all_cycles_df <- rbindlist(cycle_list, fill = TRUE)
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Note: The all_simple_cycles function returns a list of all simple cycles in the graph. Each cycle is a vector of node indices. The min and max arguments specify the minimum and maximum length of the cycles.

Alternatively, you can use the clusters function to find connected components in the graph and then use the induced_subgraph function to extract the subgraph corresponding to each component:


main.r
# Find connected components
clusters <- clusters(g)

# Loop through the components
for (i in 1:length(clusters$membership)) {
  # Extract the subgraph corresponding to the component
  subgraph <- induced_subgraph(g, clusters$membership == i)
  # Find all simple cycles in the subgraph
  cycles <- all_simple_cycles(subgraph, min = 3, max = length(V(subgraph)))
  # ... and so on
}
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This will give you a list of dataframes, each corresponding to a connected component in the graph, with the paths that form loops.

Here is a small community matrix creation example with a community as groups labeled community_1, community_2, etc:


main.r
# Create an empty graph with 10 vertices
g <- make_empty_graph(n = 10)

# Add edges between the vertices to create communities
g <- add_edges(g, c(1, 2, 1, 3, 1, 4, 2, 3, 2, 4, 3, 4))
g <- add_edges(g, c(5, 6, 5, 7, 5, 8, 6, 7, 6, 8, 7, 8))
g <- add_edges(g, c(9, 10))

# Label the communities
community <- rep(c("community_1", "community_2", "community_3"), times = c(4, 4, 2))

# Create a dataframe with the community labels
community_df <- data.frame(vertex = V(g), community = community)
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