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r - `modularity()`的正确使用和解释

转载 作者:行者123 更新时间:2023-12-04 04:32:37 25 4
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igraph ?modularity 部分给出了示例代码

g <- graph.full(5) %du% graph.full(5) %du% graph.full(5)
g <- add.edges(g, c(1,6, 1,11, 6, 11))
wtc <- walktrap.community(g)
modularity(wtc)
#[1] 0.5757575
modularity(g, membership(wtc))
#[1] 0.5757576

wtc 的输出显示:

wtc
#Graph community structure calculated with the walktrap algorithm
#Number of communities (best split): 3
#Modularity (best split): 0.5757575
#Membership vector:
# [1] 3 3 3 3 3 1 1 1 1 1 2 2 2 2 2

我对不同的部分感到困惑:

modularity(wtc)
# and
modularity(g, membership(wtc))

wtc 本身已经有了最好的拆分及其相关的模块化。为什么要在 wtc 上调用 modularitymodularity(g, membership(wtc)) 我看到的是找到特定预选拆分的模块化,这对我来说更有意义(在这种情况下是最佳拆分)。

在什么情况下您会认为这些结果会有所不同,为什么会这样,例如

g2 <- structure(list(from = structure(c(2L, 3L, 4L, 1L, 3L, 4L, 1L, 
2L, 4L, 1L, 2L, 3L), .Label = c("A", "B", "C", "D"), class = "factor"),
to = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L,
4L, 4L), .Label = c("A", "B", "C", "D"), class = "factor"),
weight = c(2L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L
)), .Names = c("from", "to", "weight"), row.names = c(2L,
3L, 4L, 5L, 7L, 8L, 9L, 10L, 12L, 13L, 14L, 15L), class = "data.frame")

g2 <- graph.data.frame(g2, vertices = unique(g2[1]))

set.seed(444)
wtc2 <- walktrap.community(g2)
modularity(wtc2)
# [1] 0.4444444
wtc2
# Graph community structure calculated with the walktrap algorithm
# Number of communities (best split): 2
# Modularity (best split): 0.4444444
# Membership vector:
# B C D A
# 2 1 1 2
modularity(g2, membership(wtc2))
# [1] -0.1666667

sessionInfo()
# R version 3.0.2 (2013-09-25)
# Platform: x86_64-apple-darwin10.8.0 (64-bit)
#
# locale:
# [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] Matrix_1.0-14 lattice_0.20-23 igraph_0.6.6 reshape2_1.2.2 ggplot2_0.9.3.1
#
# loaded via a namespace (and not attached):
# [1] colorspace_1.2-4 dichromat_2.0-0 digest_0.6.3 grid_3.0.2 gtable_0.1.2 labeling_0.2
# [7] MASS_7.3-29 munsell_0.4.2 plyr_1.8 proto_0.3-10 RColorBrewer_1.0-5 scales_0.2.3
# [13] stringr_0.6.2 tools_3.0.2

最佳答案

modularity(graph, split) 在您的 igraph 版本中不支持边权重,因此存在差异。在这种情况下,基本上假定所有边的权重都为 1。

关于r - `modularity()`的正确使用和解释,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/20313608/

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