iterating a for loop over a list of igraph objects to compare different clustering algorithms

I’m wondering if someone could help me with this for loop. Or suggest another way of getting at what I want (I know the code is a bit of a nightmare, but I’m stumped on how to make it, well, more elegant).

The problem: I have some network objects in a list; I would like to compare some community detection algorithms for those networks. And I would like to get at that information in one go (unlike the toy example here the real list I have has many networks in it). The following toy example gets me close to the information I want, but it does not list the networks in the final output. What do I need to do in order to also include the name of the networks in the final data frame?

library(graph)
library(igraphdata)
library(igraph)

# create a couple of networks
data(karate)
g2 <- erdos.renyi.game(50, .2)

# stick the networks in a list
list_of_networks <- list(karate, g2)

# get all the community detection algorithms 
clusters <- grep("^cluster_", ls("package:igraph"), value=TRUE)[-1] 

# get rid of the algorithms I don't want
clusters <- clusters[!grepl("spinglass|fluid|leiden|walktrap|infomap|prop|optimal", clusters)]

# for loop stuff

# there are two networks in the network list
x <- 1:2

# a container for my for loop results
container <- vector("list", length = 2)

# nested for loop
for (cluster in clusters) {
 
 for (i in x) {
  
  result <- t(c(
   
   modularity(do.call(cluster, list(list_of_networks[[i]]))), 
   length(do.call(cluster, list(list_of_networks[[i]]))),
   cluster
   
  ))
  
  container[[i]] <- rbind(result, container[[i]])
 }
}

container %>%
 map(~ as_tibble(.)) %>%
 bind_rows() %>% 
 rename(modularity = 1,
        num_communities = 2,
        algorithm = 3)

You could use map_df's .id argument:

library(tidyverse)

container %>% 
  map_df(~ as_tibble(.) %>% 
           rename(modularity = 1,
                  num_communities = 2,
                  algorithm = 3), 
         .id = "name")

This returns

# A tibble: 6 × 4
  name  modularity        num_communities algorithm            
  <chr> <chr>             <chr>           <chr>                
1 1     0.443854125672307 4               cluster_louvain      
2 1     0.4366391184573   5               cluster_leading_eigen
3 1     0.434521466989    3               cluster_fast_greedy  
4 2     0.224687153369057 5               cluster_louvain      
5 2     0.197245699823462 4               cluster_leading_eigen
6 2     0.193293130653502 4               cluster_fast_greedy  

so your networks are named 1 and 2.

To get better information, we add a line to your code

container <- vector("list", length = 2)
# Add names here
names(container) <- unlist(lapply(list_of_networks, \(x) x$name))
# or
# names(container) <- sapply(list_of_networks, \(x) x$name)

Now

container %>% 
  map_df(~ as_tibble(.) %>% 
           rename(modularity = 1,
                  num_communities = 2,
                  algorithm = 3), 
         .id = "name")

returns

# A tibble: 6 × 4
  name                          modularity        num_communities algorithm            
  <chr>                         <chr>             <chr>           <chr>                
1 Zachary's karate club network 0.444903581267218 4               cluster_louvain      
2 Zachary's karate club network 0.4366391184573   5               cluster_leading_eigen
3 Zachary's karate club network 0.434521466989    3               cluster_fast_greedy  
4 Erdos-Renyi (gnp) graph       0.210112108800672 4               cluster_louvain      
5 Erdos-Renyi (gnp) graph       0.188379542113001 2               cluster_leading_eigen
6 Erdos-Renyi (gnp) graph       0.21151675068263  3               cluster_fast_greedy