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我有一个无法优化的问题，我确信 igraph 或 tidy graphs 必须已经拥有此功能，或者必须有更好的方法来做到这一点。我正在使用 R 和 igraph 来做到这一点，但可能 tidygraphs 也可以完成这项工作。

问题：如何将超过 200 万条边（节点 1 - 链接到 - 节点 2）的列表定义为各自独立的网络，然后将网络定义为权重最高的节点类别。

数据：

边缘：

从 至
1 2
3 4
5 6
7 6
8 6

这在实际示例中创建了 3 个网络 NB，我们有循环和多个节点往返节点（这就是我使用 igraph 的原因，因为它可以轻松处理这些）。

网络

数据：节点类别：

ID 猫 重量
1 交通意外 10
2 虐待 50
3 虐待 50
4 超速 5
5 谋杀 100
6 虐待 50
7 超速 5
8 虐待 50

决赛桌：决赛桌对每个节点进行分类，并用节点的最大类别标记每个网络

ID idcat 网络标识 网络猫
1 交通意外 1 50
2 虐待 1 50
3 虐待 2 50
4 超速 2 50
5 谋杀 3 100
6 虐待 3 100
7 超速 3 100
8 虐待 3 100

当前的迭代解决方案和代码：如果没有更好的解决方案，那么也许我们可以加快迭代速度？

library(tidyverse)
library(igraph)
library(purrr) #might be an answer
library(tidyverse)
library(tidygraph) #might be an answer

from <- c(1,3,5,7,8)
to <- c(2,4,6,6,6)
edges <- data.frame(from,to)

id <- c(1,2,3,4,5,6,7,8)
cat <- c("traffic accident","abuse","abuse","speeding","murder","abuse","speeding","abuse")
weight <- c(10,50,50,5,100,50,5,50)

details <- data.frame(id,cat,weight) 

g <- graph_from_data_frame(edges)# we can add the vertex details here as well g <- 
graph_from_data_frame(edges,vertices=details) but we join these in later
plot(g)

dg <- decompose(g)# decomposing the network defines the separate networks 

networks <- data.frame(id=as.integer(),
                   network_id=as.integer())

for (i in 1:length(dg)) { # this is likely too many to do at once. As the networks are already defined we can split this into chunks. There is a case here for parellisation
  n <- dg[[i]][1] %>% # using the decomposed list of lists from i graph. There is an issue here as the list comes back with the node as an index. I can't find an easier way to get this out
    as.data.frame() %>% # I can't work a way to bring out the data without changing to df and then using row names
    row.names() %>% # and this returns a vector
    as.data.frame() %>% 
    rename(id=1) %>% 
    mutate(network_id = i,
           id=as.integer(id))

  networks <-bind_rows(n,networks)
}  

networks <- networks %>% 
  inner_join(details) # one way to bring in details

n_weight <- networks %>%
  group_by(network_id) %>% 
  summarise(network_weight=max(weight))

networks <- networks %>% 
  inner_join(n_weight)

networks # final answer

filtered_n <- networks %>% 
  filter(network_weight==100) %>% 
  select(network_id) %>% 
  distinct()#this brings out just the network ID's of whatever we happen to want

filtered_n <- networks %>% 
  filter(network_id %in% filtered_n_id$network_id)

edges %>% 
  filter(from %in% filtered_n$id | to %in% filtered_n$id ) %>% 
  graph_from_data_frame() %>% 
  plot() # returns only the network/s that we want to view
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1 回答 1

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这是一个仅使用 igraph 和 base R 的解决方案。

networkid <- components(g)$membership
Table <- aggregate(id, list(networkid),  function(x) { max(weight[x]) })
networkcat <-  Table$x[networkid]
Final <- data.frame(id, idcat=cat, networkid, networkcat)

Final
  id            idcat networkid networkcat
1  1 traffic accident         1         50
2  2            abuse         1         50
3  3            abuse         2         50
4  4         speeding         2         50
5  5           murder         3        100
6  6            abuse         3        100
7  7         speeding         3        100
8  8            abuse         3        100
于 2020-12-15T22:11:44.790 回答