r - Efficient way to analysis neighbours of subsets of nodes in large graph -

i have graph of 6 million of nodes such

require(igraph)  # graph of 1000 nodes g <- ba.game(1000) 

with following 4 attributes defined each node

# attributes v(g)$attribute1 <- v(g) %in% sample(v(g), 20) v(g)$attribute2 <- v(g) %in% sample(v(g), 20) v(g)$attribute3 <- v(g) %in% sample(v(g), 20) v(g)$attribute4 <- v(g) %in% sample(v(g), 20) 

among nodes have subset of 12,000 of particular interest:

# subset of 100 nodes v(g)$subset <- v(g) %in% sample(v(g), 100) 

what want obtain analysis (count) of neighbourhood of subset. is, want define

v(g)$neigh.attr1 <- rep(na, vcount(g)) v(g)$neigh.attr2 <- rep(na, vcount(g)) v(g)$neigh.attr3 <- rep(na, vcount(g)) v(g)$neigh.attr4 <- rep(na, vcount(g)) 

such na replaced every node in subset corresponding count of neighbouring nodes v(g)$attribute{1..4}==true.

i can create list of neighbourhood of interest

neighbours <- neighborhood(g, order = 1, v(g)[v(g)$subset==true], mode = "out") 

but can't think of efficient way iterate on every neighbours , compute statistics each of 4 attributes. indeed way i've came loop given size of original graph takes long:

subset_indices <- as.numeric(v(g)[v(g)$subset==true]) (i in 1:length(neighbours)) {     v(g)$neigh.attr1[subset_indices[i]] <- sum(v(g)$attribute1[neighbours[[i]]])     v(g)$neigh.attr2[subset_indices[i]] <- sum(v(g)$attribute2[neighbours[[i]]])     v(g)$neigh.attr3[subset_indices[i]] <- sum(v(g)$attribute3[neighbours[[i]]])     v(g)$neigh.attr4[subset_indices[i]] <- sum(v(g)$attribute4[neighbours[[i]]]) }