igraph将图分割为簇。

Question

igraph将图分割为簇。

4

我有一些无向图：

带有数据：

get.data.frame(out)
   from   to  value sourceID targetID
1    74   80 0.2829   255609   262854
2    74   61 0.2880   255609   179585
3    80 1085 0.2997   262854  3055482
4  1045 1046 0.1842  2970629  2971615
5  1046 1085 0.2963  2971615  3055482
6  1046 1154 0.2714  2971615  3087803
7  1085 1154 0.2577  3055482  3087803
8  1085 1187 0.2850  3055482  3101131
9  1085 1209 0.2850  3055482  3110186
10 1154 1243 0.2577  3087803  3130848
11 1154 1187 0.2305  3087803  3101131
12 1154 1209 0.2305  3087803  3110186
13 1154 1244 0.2577  3087803  3131379
14 1243 1187 0.1488  3130848  3101131
15 1243 1209 0.1488  3130848  3110186
16 1243 1244 0.1215  3130848  3131379
17 1243 1281 0.2997  3130848  3255811

> out
IGRAPH UN-- 12 17 -- 
+ attr: name (v/c), color (v/n), value (e/n), sourceID (e/n), targetID (e/n)
+ edges (vertex names):
 [1] 74  --80   74  --61   80  --1085 1045--1046 1046--1085 1046--1154 1085--1154 1085--1187 1085--1209 1154--1243 1154--1187 1154--1209
[13] 1154--1244 1243--1187 1243--1209 1243--1244 1243--1281

我正在使用两种社区检测方法。

使用edge.betweenness.community，我可以通过计算边介数来分割社区。

wc <- edge.betweenness.community(out, weights = E(out)$value, directed = FALSE, bridges=TRUE)
plot(wc, out)

使用cluster_optimal，我可以通过最大化模块度来分割社区:

co <- cluster_optimal(out, weights = E(out)$value)
plot(co, out)

关于 co 的数据：

> co
IGRAPH clustering optimal, groups: 3, mod: 0.27
+ groups:
  $`1`
  [1] "74" "80" "61"

  $`2`
  [1] "1045" "1046" "1085" "1154" "1187" "1209"

  $`3`
  [1] "1243" "1244" "1281"

在这一点上，我想将这些图分成单独的社区。这是通过切断那些被突出显示为红色的边来完成的。

对于edge.betweenness.community，我可以通过返回需要切断的边来实现此操作：

wc$removed.edges
[1]  3  6  5 17 14 11  8 15  7 12  1  2  4  9 10 13 16

我可以然后剪掉那些边缘：

g2<-delete.edges(out, wc$removed.edges[seq(length=which.max(mods)-1)])
plot(g2)

但如果我尝试对cluster_optimal这样做：

co$removed.edges
NULL

看起来 cluster_optimal 没有属性 removed$edges

然后我查看了文档 (http://igraph.org/r/doc/communities.html) 并尝试了 cut_at 和 cutat:

> cutat(co, 3)
Error in cutat(co, 3) : Not a hierarchical communitity structure
> cut_at(co, 3)
Error in cut_at(co, 3) : Not a hierarchical communitity structure

这也没有起作用，尽管co应该是一个社区对象。

我该如何像使用edge.betweenness.community一样将我的cluster_optimal图分割成单独的社区？

尝试了发布的建议后，当我尝试执行时

crossing(co, out)

我收到了错误信息：

Error: Each element must be either an atomic vector or a data     frame
.Problems: co, out.

R version 3.4.0 (2017-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.0
LAPACK: /usr/lib/lapack/liblapack.so.3.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plotly_4.7.0        ggplot2_2.2.1       tidyr_0.6.2         dtplyr_0.0.2        data.table_1.10.4   DT_0.2             
 [7] reshape2_1.4.2      igraph_1.0.1        dplyr_0.5.0         htmlwidgets_0.8     networkD3_0.4       htmltools_0.3.6    
[13] formattable_0.2.0.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.10      compiler_3.4.0    plyr_1.8.4        tools_3.4.0       digest_0.6.12     jsonlite_1.4      evaluate_0.10    
 [8] tibble_1.3.0      gtable_0.2.0      viridisLite_0.2.0 lattice_0.20-35   Matrix_1.2-10     DBI_0.6-1         stringr_1.2.0    
[15] httr_1.2.1        knitr_1.15.1      rprojroot_1.2     grid_3.4.0        R6_2.2.0          rmarkdown_1.5     purrr_0.2.2      
[22] magrittr_1.5      backports_1.0.5   scales_0.4.1      assertthat_0.2.0  colorspace_1.3-2  stringi_1.1.5     lazyeval_0.2.0   
[29] munsell_0.4.3

- Chuck

1个回答

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- emilliman5 · Accepted Answer

看起来对于所有社区，您可以使用crossing函数来获取哪些边界穿越社区（请参阅社区对象的文档）。

library(igraph)

out <- data.frame(To = sample(1:10, 20, replace = T), From =sample(c(1,3,5,7,9), 20, replace = T))

out <- graph_from_edgelist(as.matrix(out), directed = F)
co <- cluster_optimal(out, weights = rpois(20, 2))
coGrph <- delete_edges(out, E(out)[crossing(co, out)])

par(mfrow=c(1,2))
plot(co, out, main="Cluster Optimal Communities")
plot(coGrph, main="Communities split")

会话信息

R version 3.3.0 (2016-05-03)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.12.5 (unknown)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] igraph_1.0.1

loaded via a namespace (and not attached):
[1] magrittr_1.5 tools_3.3.0