我有一个包含 4 components 的图表。现在，我想根据 all components 在 size of the membership 之间添加一条边。

例如，下图包含 4 components。

首先，我将连接 all components with only one edge and take the edge randomly。我可以用这个代码来做

graph1 <- graph_from_data_frame(g,directed = FALSE)
E(graph1)$weight <- g$new_ssp
cl <- components(graph1)

graph2 <- with(
  stack(membership(cl)),add.edges(
    graph1,c(combn(sapply(split(ind,values),sample,size = 1),2)),weight = runif(choose(cl$no,2))
  )
)

其次，现在我想在 component-1 and component-2 之间添加一条边。我想在 2 components 和 rest of the component will be present in the new graph from the previous graph 之间添加一条边。

例如，在 component-1 and component-2 之间添加一条边后，新图将包含 3 component 1st (component-1 and component-2 as a 1 component because we added 1 edge),2nd (component-3 from the main graph),and 3rd (component-4 from the main graph)。我可以用这个代码来做

dg <- decompose.graph(graph1)
graph3 <- (dg[[1]] %u% dg[[2]])

component_subgraph_1 <- components(graph3)

graph2 <- with(
  stack(membership(component_subgraph_1)),weight = 0.01))

图：

所有组合都相同。例如，component-1 and component-3、component-1 and component-4、component-2 and component-3、component-2 and component-4、component-3 and component-4。

但是，编写代码并手动更改dg[[1]]、dg[[2]] 等是不可行的。此外，我的实际数据集包含很多组件。所以，实际上，这是不可能的。 知道吗，我如何自动执行此操作？

实际上，我有一个评分函数（比如最短路径）。所以，我想在adding all components，或after adding only 2 components、after adding only 3 components和so on之后查看分数！类似于 greedy algorithms。

可重现的数据：

g <- structure(list(query = structure(c(1L,1L,2L,3L,4L,5L,5L),.Label = c("ID_00104","ID_00136","ID_00169","ID_00178","ID_00180"),class = "factor"),target = structure(c(16L,19L,20L,9L,6L,11L,13L,15L,8L,10L,14L,7L,12L,17L,18L
               ),.Label = c("ID_00169","ID_00288","ID_00324","ID_00394","ID_00663","ID_00790","ID_00846","ID_00860","ID_00910","ID_00959","ID_01013","ID_01047","ID_01130","ID_01222","ID_01260","ID_06663","ID_06781","ID_06786","ID_06791","ID_09099"),new_ssp = c(0.654172560113154,0.919096895578551,0.925821596244131,0.860406091370558,0.746376811594203,0.767195767195767,0.830379746835443,0.661577608142494,0.707520891364902,0.908193484698914,0.657118786857624,0.687664041994751,0.68586387434555,0.874513618677043,0.836646499567848,0.618361836183618,0.684163701067616,0.914728682170543,0.876297577854671,0.732707087959009,0.773116438356164)),row.names = c(NA,-21L),class = "data.frame")

提前致谢。