通过使用igraph(R)组合入射顶点的属性来创建边缘属性 [英] Creating edge attributes by combining attributes of incident vertices using igraph (R)

问题描述

对于图形中的每个边，我想添加一个数字属性(权重)，它是入射顶点的属性(概率)的乘积.我可以通过遍历边缘来做到这一点；即:

For each edge in a graph I would like to add an numeric attribute (weight) that is the product of an attribute (probability) of the incident vertices. I can do it by looping over the edges; that is:

    for (i in E(G)) {
      ind <- V(G)[inc(i)]
      p <- get.vertex.attribute(G, name = "prob", index=ind)
      E(G)[i]$weight <- prod(p)
    }

但是，这对于我的图来说很慢(| V |〜= 20,000和| E |〜= 200,000).有没有更快的方法来执行此操作?

However, this is qute slow for my graph (|V| ~= 20,000 and |E| ~= 200,000). Is there a faster way to do this operation?

推荐答案

这可能是最快的解决方案.关键是矢量化.

Here is probably the fastest solution. The key is to vectorize.

library(igraph)
G <- graph.full(45)
set.seed(1)
V(G)$prob <- pnorm(vcount(G))

## Original solution
system.time(
  for (i in E(G)) {
    ind <- V(G)[inc(i)]
    p <- get.vertex.attribute(G, name = "prob", index=ind)
    E(G)[i]$wt.1 <- prod(p)
  }
)
#>    user  system elapsed 
#>   1.776   0.011   1.787 

## sapply solution
system.time(
  E(G)$wt.2 <- sapply(E(G), function(e) prod(V(G)[inc(e)]$prob))
)
#>    user  system elapsed 
#>   1.275   0.003   1.279 

## vectorized solution 
system.time({
  el <- get.edgelist(G)
  E(G)$wt.3 <- V(G)[el[, 1]]$prob * V(G)[el[, 2]]$prob
})
#>    user  system elapsed 
#>   0.003   0.000   0.003 

## are they the same?
identical(E(G)$wt.1, E(G)$wt.2)
#> [1] TRUE
identical(E(G)$wt.1, E(G)$wt.3)
#> [1] TRUE

矢量化解决方案的速度似乎快了500倍，尽管需要更多更好的测量来更精确地进行评估.

The vectorized solution seems to be about 500 times faster, although more and better measurements would be needed to evaluate this more precisely.

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