R: Writing Graph Based Functions

I have a graph network for a group of friends that looks like this:

set.seed(123)
library(igraph)

# Define a vector of names
names <- c("John", "Alex", "Jason", "Matt", "Tim", "Luke", "Shawn", "Henry", "Steven", "Scott", "Adam", "Jeff", "Connor", "Peter", "Andrew", "Dave", "Daniel", "Benjamin", "Joseph", "Martin")

# Create an empty graph with 20 nodes
g <- make_empty_graph(20)

# Add random edges between nodes to represent friendships
set.seed(123)  # for reproducibility
num_edges <- 40
edge_list <- sample(names, size = num_edges * 2, replace = TRUE)
edge_list <- matrix(edge_list, ncol = 2, byrow = TRUE)
g <- graph_from_edgelist(edge_list, directed = FALSE)

# Set the node names to be the names vector
V(g)$name <- names

# Plot the graph
plot(g, vertex.label.cex = 0.7, vertex.label.color = "black", vertex.label.dist = 2)

Each of these friends has a certain number of cookies :

cookies = data.frame(names = names, cookies = as.integer(rnorm(length(names), 20,10)))

I am trying to write a function with the following steps:

• Step 1: Randomly select a (original) node
• Step 2: Random select a "radius" (degree)
• Step 3: Randomly select a number of nodes to search within the radius of the original node (but original node must be connected to these nodes)
• Step 4: Add the number of cookies of the original node and all cookies from the nodes selected in Step 3
• Step 5: IF the total in Step 4 is greater than 50 or less than 100 : END . ELSE Restart from Step 1
• Step 6: Subtract all selected names from original graph

As an an example:

• Suppose I select Scott
• Radius = 3
• Number of Nodes = 4 (Matt, Jason, John, Adam) - notice how all these names can be traced back to Scott
• Add number of cookies for Scott, Matt, Jason, John , Adam (25 + 16 + 16 + 14 + 16 = 87)
• Since 87 is greater than 50 and less than 100 - finish
• Remove "Scott, Matt, Jason, John , Adam" from the graph

I tried to write the R code as follows:

#STEP 1
original_node =  sample(V(g), 1)$name

#STEP 2: Find the degree of the farthest node from the original node to be the max "ceiling" 

# Calculate the distance from original_node to all other nodes
distances <- distances(g, original_node)

# Find the node furthest away from original node
max_distance <- max(distances)
furthest_node <- which(distances == max_distance)

# Find the degree of the furthest node
furthest_degree <- max(as.numeric(degree(g, furthest_node)))
radius <- sample(0:furthest_degree, 1)

# STEP 3 : 

# Find the neighbors of original node with degrees ranging from 1 to max
neighbors_degree_1_to_max <- induced.subgraph(g, unlist(ego(g, order=2, nodes=original_node)))

# Find the number of neighbors with degrees ranging from 1 to max
num_neighbors_degree_1_to_max <- length( neighbors_degree_1_to_max)

# randomly select number of nodes to search
nodes_to_search <- sample(0:num_neighbors_degree_1_to_max, 1)

# randomly select individual nodes within nodes_to_search
# stuck from here

But I am not sure how to proceed from here, can someone please show me what to do?

Thanks!

References:

• https://stackoverflow.com/questions/75588946/r-subtracting-a-subgraph-from-a-graph

You can do like this

# add attribute `cookie` to vertices
V(g)$cookie <- cookies$cookies

# loop till you find the desired clique
repeat {
  rand_neighbours <- ego(g, order = 2, nodes = original_node) %>%
    unlist() %>%
    sample(sample.int(length(.) + 1, 1) - 1)

  clique_cookie_sum <- sum(V(g)$cookie[rand_neighbours])

  if (clique_cookie_sum > 50 & clique_cookie_sum < 100) {
    grp <- subgraph(g, rand_neighbours)
    g <- subgraph(g, !V(g) %in% rand_neighbours) # update `g` by subtracting `grp` 
    break
  }
}

and you will see

> V(grp)
+ 4/4 vertices, named, from 52d6fc7:
[1] Steven Adam   Andrew Martin
> V(grp)$cookie
[1] 13 12 20 23
> sum(V(grp)$cookie)
[1] 68