I was reading about this over here: https://en.wikipedia.org/wiki/Valeriepieris_circle . This is a problem where the task is to find the smallest possible circle that contains half of the world's population. I am trying to replicate this task myself as a learning exercise.

To begin, instead of using an actual world map - to simplify things, I imagined a rectangular world. This rectangular world is actually a network graph made of 1000 nodes, such that each node is only connected to all of its immediate neighbors only once. The nodes in the graph have id's from 1 to 1000, and each node is assigned a random value to represent the population at that point.

Here is how everything looks like:

library(igraph)

width <- 30
height <- 20
num_nodes <- width * height

# Create a grid
x <- rep(1:width, each = height)
y <- rep(1:height, times = width)

g <- make_empty_graph(n = num_nodes, directed = FALSE)

# Function to get node index
get_node_index <- function(i, j) (i - 1) * height + j

# Add edges
edges <- c()
for(i in 1:width) {
   for(j in 1:height) {
      current_node <- get_node_index(i, j)
    
      # Connect to right neighbor
      if(i < width) edges <- c(edges, current_node, get_node_index(i + 1, j))
    
      # Connect to bottom neighbor
      if(j < height) edges <- c(edges, current_node, get_node_index(i, j + 1))
   }
}

g <- add_edges(g, edges)

V(g)$x <- x
V(g)$y <- y

par(mfrow=c(1,2))

V(g)$name <- 1:num_nodes
plot(g, vertex.size = 7, vertex.label = V(g)$name, vertex.label.cex = 0.6, main = "Map with         Node Indices")

V(g)$value <- sample(1:100, num_nodes, replace = TRUE)
plot(g, vertex.size = 7, vertex.label = V(g)$value, vertex.label.cex = 0.6, main = "Map with     Population Values")

It is quite difficult to work with circles. Instead of circles, I decided to work with squares made of 4 nodes. My task is now to find the square with the largest node sums. I tried to do make an exhaustive list of all squares and record their sums:

library(dplyr)
squares <- list()
square_id <- 1

for(i in 1:(width-1)) {
for(j in 1:(height-1)) {
    top_left <- get_node_index(i, j)
    top_right <- get_node_index(i+1, j)
    bottom_left <- get_node_index(i, j+1)
    bottom_right <- get_node_index(i+1, j+1)
    
        square <- c(top_left, top_right, bottom_left, bottom_right)
        squares[[square_id]] <- square
        square_id <- square_id + 1
    }
}

result_df <- data.frame(
    square_id = seq_along(squares),
    nodes_id_selected = sapply(squares, function(s) paste(s, collapse = ", ")),
    value = sapply(squares, function(s) sum(V(g)$value[s]))
)

print(head(result_df %>% arrange(-value)))

 square_id  nodes_id_selected value
       334 351, 371, 352, 372   365
        51     53, 73, 54, 74   350

Question

Is there a way to generalize this approach for any sided shape? e.g. triangle, hexagon, etc. Is it possible to write a function that can carry out these comparisons for any sided shape?

In your specific example, you can use subgraph_isomorphisms to find all rings of length 4 (it should be 6 if you are searching for all hexagons, and so on so forth), and then induced_subgraph from g to check the sum of the vertex values.

sg <- subgraph_isomorphisms(make_ring(4), g)
lst <- unique(lapply(sg, \(x) sort(names(x))))
out <- do.call(
  rbind,
  lapply(
    lst,
    \(v) data.frame(
      node_id = toString(v),
      value = sum(V(induced_subgraph(g, v))$value)
    )
  )
)

and head(out) shows

> head(out)
       node_id value
1 1, 2, 21, 22   208
2 2, 22, 23, 3   233
3 23, 24, 3, 4   111
4 24, 25, 4, 5   158
5 25, 26, 5, 6   254
6 26, 27, 6, 7   253

and the size of out is

> head(out)
       node_id value
1 1, 2, 21, 22   208
2 2, 22, 23, 3   233
3 23, 24, 3, 4   111
4 24, 25, 4, 5   158
5 25, 26, 5, 6   254
6 26, 27, 6, 7   253
> dim(out)
[1] 551   2