igraph: summarize each node's neighbours characteristics

Question

With an igraph object I would like to capture some features of each node's neighbours, for example the average degree of its neighbours.

I come up with this code, which is inelegant and quite slow. How should I rethink it for large and complex networks?

library(igraph)

# Toy example
set.seed(123)
g <- erdos.renyi.game(10,0.2)

# Loop to calculate average degree of each node's neighbourhood
s <- character(0)
for(i in 1:gorder(g)){
  n <- ego_size(g, nodes = i, order = 1, mindist = 1)
  node_of_interest <- unique(unlist(ego(g, nodes = i, order = 1, mindist = 1)))
  m <- mean(degree(g, v = node_of_interest, loops = TRUE, normalized = FALSE)-1)
  
  s <- rbind(s,data.frame(node = i, neighbours = n, mean = m))

}

Answer 1

Expanding the data structure with rbind in a loop can get quite slow in R, because at every step it needs to allocate the space for the new object, and then copy it (see section 24.6 here). Also, you might be computing the degree of a node many times, if it s the neighbor of multiple nodes.

A possibly better alternative could be:

# add vertex id (not really necessary)
V(g)$name <- V(g) 

# add degree to the graph
V(g)$degree <- degree(g, loops = TRUE, normalized = FALSE)

# get a list of neighbours, for each node
g_ngh <- neighborhood(g, mindist = 1) 

# write a function that gets the means                       
get.mean <- function(x){
  mean(V(g)$degree[x]-1)
}

# apply the function, add result to the graph
V(g)$av_degr_nei <- sapply(g_ngh, get.mean)


# get data into dataframe, if necessary
d_vert_attr <- as_data_frame(g, what = "vertices")

d_vert_attr
   name degree av_degr_nei
1     1      0         NaN
2     2      1   2.0000000
3     3      2   1.0000000
4     4      1   1.0000000
5     5      2   1.0000000
6     6      1   1.0000000
7     7      3   0.6666667
8     8      1   0.0000000
9     9      1   0.0000000
10   10      0         NaN