Question

我有一个简单的问题。我试图在图形中设置边缘的权重，我想对其进行一些计算。我的工作代码如下： for(e in E(g)){ E(g)[e]$weight <- 1/(degree(g, v = V(g)[ends(g, e)][2], mode = "in")) }
对于小图表，这很好。但是对于具有105K边缘的图形，这需要太长时间（65分钟!!）有没有更有效的方法来做到这一点，我怎样才能更快地做到这一点？非常感谢提前！

Answer 1

通过矢量化代码可以使计算速度更快，从而完全避免for循环 - 这会更频繁地重新计算每个节点的程度：

library(igraph)

# A small toy graph to demonstrate
set.seed(234)
g <- random.graph.game(10, .25, directed = T)

# Your approach for constructing weights
for(e in E(g)){
  E(g)[e]$weight <- 1/(degree(g, v = V(g)[ends(g, e)][2], mode = "in"))
}

# Vectorized version
res <- 1/degree(g, v = V(g)[ends(g, E(g))[,2]], mode = "in")

# We test to make sure the results are the same
all.equal(E(g)$weight, res)
#> [1] TRUE

# To show how much faster it is lets make a big graph with ~100k edges
set.seed(42)
g <- random.graph.game(1000, .1, directed = T)
length(E(g))
#> [1] 99307

microbenchmark::microbenchmark(E(g)$weight <- 1/degree(g, v = V(g)[ends(g, E(g))[,2]], mode = "in"))
#> Unit: milliseconds
#>                                                                   expr
#>  E(g)$weight <- 1/degree(g, v = V(g)[ends(g, E(g))[, 2]], mode = "in")
#>       min       lq     mean   median       uq      max neval
#>  15.45099 57.88469 54.22476 59.22422 60.43034 106.6269   100

新版本在99,307边缘大约需要60毫秒。

提高图Igraph R

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