Packages = c(
'tidyverse',
'readxl',
'psych',
'dplyr',
'igraph',
'network',
'intergraph',
'extrafont',
'sna'
)
for(p in Packages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}
## Loading required package: tidyverse
## ── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.8
## ✔ tidyr 0.8.2 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## Loading required package: readxl
## Loading required package: psych
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## Loading required package: igraph
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:purrr':
##
## compose, simplify
## The following object is masked from 'package:tidyr':
##
## crossing
## The following object is masked from 'package:tibble':
##
## as_data_frame
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
## Loading required package: network
## network: Classes for Relational Data
## Version 1.13.0.1 created on 2015-08-31.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
##
## Attaching package: 'network'
## The following objects are masked from 'package:igraph':
##
## %c%, %s%, add.edges, add.vertices, delete.edges,
## delete.vertices, get.edge.attribute, get.edges,
## get.vertex.attribute, is.bipartite, is.directed,
## list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute
## Loading required package: intergraph
## Loading required package: extrafont
## Registering fonts with R
## Loading required package: sna
## Loading required package: statnet.common
##
## Attaching package: 'statnet.common'
## The following object is masked from 'package:base':
##
## order
## sna: Tools for Social Network Analysis
## Version 2.4 created on 2016-07-23.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.
##
## Attaching package: 'sna'
## The following objects are masked from 'package:igraph':
##
## betweenness, bonpow, closeness, components, degree,
## dyad.census, evcent, hierarchy, is.connected, neighborhood,
## triad.census
# System
set.seed(12345)
Sys.info()
## sysname
## "Darwin"
## release
## "18.2.0"
## version
## "Darwin Kernel Version 18.2.0: Fri Oct 5 19:41:49 PDT 2018; root:xnu-4903.221.2~2/RELEASE_X86_64"
## nodename
## "Chungils-MacBook-Pro.local"
## machine
## "x86_64"
## login
## "chadchae"
## user
## "chadchae"
## effective_user
## "chadchae"
sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## 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] sna_2.4 statnet.common_4.1.4 extrafont_0.17
## [4] intergraph_2.0-2 network_1.13.0.1 igraph_1.2.2
## [7] psych_1.8.10 readxl_1.1.0 forcats_0.3.0
## [10] stringr_1.3.1 dplyr_0.7.8 purrr_0.2.5
## [13] readr_1.1.1 tidyr_0.8.2 tibble_1.4.2
## [16] ggplot2_3.1.0 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
## [5] htmltools_0.3.6 yaml_2.2.0 rlang_0.3.0.1 pillar_1.3.0
## [9] foreign_0.8-71 glue_1.3.0 withr_2.1.2 modelr_0.1.2
## [13] bindrcpp_0.2.2 bindr_0.1.1 plyr_1.8.4 munsell_0.5.0
## [17] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2 coda_0.19-2
## [21] evaluate_0.12 knitr_1.20 parallel_3.5.1 Rttf2pt1_1.3.7
## [25] broom_0.5.0 Rcpp_1.0.0 scales_1.0.0 backports_1.1.2
## [29] jsonlite_1.5 mnormt_1.5-5 hms_0.4.2 digest_0.6.18
## [33] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2 cli_1.0.1
## [37] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1 extrafontdb_1.0
## [41] crayon_1.3.4 pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
## [45] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1 rstudioapi_0.8
## [49] R6_2.3.0 nlme_3.1-137 compiler_3.5.1
Packages
## [1] "tidyverse" "readxl" "psych" "dplyr" "igraph"
## [6] "network" "intergraph" "extrafont" "sna"
Sys.time()
## [1] "2018-12-12 01:02:17 EST"
# 한글설정
Sys.setlocale("LC_CTYPE", "ko_KR.UTF-8")
## [1] "ko_KR.UTF-8"
require(extrafont) ## 잊지마세요 package 불러오기!
font_import()
## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]
## Exiting.
fonts()
## [1] ".Keyboard" "System Font"
## [3] "Andale Mono" "Apple Braille"
## [5] "AppleMyungjo" "Arial Black"
## [7] "Arial" "Arial Narrow"
## [9] "Arial Rounded MT Bold" "Arial Unicode MS"
## [11] "Bodoni Ornaments" "Bodoni 72 Smallcaps"
## [13] "" "Brush Script MT"
## [15] "Comic Sans MS" "Courier New"
## [17] "DIN Alternate" "DIN Condensed"
## [19] "Georgia" "Impact"
## [21] "Khmer Sangam MN" "Lao Sangam MN"
## [23] "Luminari" "Microsoft Sans Serif"
## [25] "Tahoma" "Times New Roman"
## [27] "Trattatello" "Trebuchet MS"
## [29] "Verdana" "Webdings"
## [31] "Wingdings" "Wingdings 2"
## [33] "Wingdings 3" "Courant"
## [35] "DejaVu Sans" "DejaVu Sans Light"
## [37] "DejaVu Sans Condensed" "DejaVu Sans Mono"
## [39] "DejaVu Serif" "DejaVu Serif Condensed"
## [41] "ESSTIXEight" "ESSTIXEleven"
## [43] "ESSTIXFifteen" "ESSTIXFive"
## [45] "ESSTIXFour" "ESSTIXFourteen"
## [47] "ESSTIXNine" "ESSTIXOne"
## [49] "ESSTIXSeven" "ESSTIXSeventeen"
## [51] "ESSTIXSix" "ESSTIXSixteen"
## [53] "ESSTIXTen" "ESSTIXThirteen"
## [55] "ESSTIXThree" "ESSTIXTwelve"
## [57] "ESSTIXTwo" "Helonia"
## [59] "MaplePi" "MT Extra"
## [61] "MT Extra Tiger" "Noto Mono"
## [63] "Noto Sans" "Noto Sans UI"
## [65] "Symbol" "SymbolPi"
## [67] "Symbol Tiger" "Symbol Tiger Expert"
## [69] "Thames" "Tiger"
## [71] "Tiger Expert"
par(family='AppleMyungjo')
theme_set(theme_gray(base_family='AppleMyungjo'))
# Connection
## Node data
node <- read_excel("../data/node.xlsx")
## Network Data
i_fi<-igraph::read_graph("../data/fi.graphml", format = "graphml")
i_fr<-igraph::read_graph("../data/fr.graphml", format = "graphml")
i_fs<-igraph::read_graph("../data/fs.graphml", format = "graphml")
i_i<-igraph::read_graph("../data/i.graphml", format = "graphml")
n_fi <- asNetwork(i_fi)
n_fr <- asNetwork(i_fi)
n_fs <- asNetwork(i_fi)
n_fi <- asNetwork(i_fi)
summary(n_fi)
## Network attributes:
## vertices = 86
## directed = TRUE
## hyper = FALSE
## loops = TRUE
## multiple = FALSE
## bipartite = FALSE
## total edges = 1093
## missing edges = 0
## non-missing edges = 1093
## density = 0.1477826
##
## Vertex attributes:
##
## b:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 11.25 20.50 25.42 33.00 121.00
##
## g:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 1 10 101 102 105 109 113 114 115 118
## 1 1 1 1 1 1 1 1 1 1
##
## In.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 11.00 12.71 17.00 54.00
##
## label:
## character valued attribute
## attribute summary:
##
## 86
##
## Out.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.25 8.50 12.71 19.00 82.00
##
## r:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## size:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 10 10 10 10 10
## vertex.names:
## character valued attribute
## 86 valid vertex names
##
## x:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -588.79 -269.83 31.12 14.95 283.65 549.75
##
## y:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -523.540 -295.536 -50.040 6.173 313.836 581.694
##
## Edge attributes:
##
## Edge.Label:
## character valued attribute
## attribute summary:
## F I
## 274 819
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 10249 10250 10253 10255 10256 10261 10264 10269 10271 10274
## 1 1 1 1 1 1 1 1 1 1
##
## weight:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.483 5.000 12.000
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 2
## [2,] 1 3
## [3,] 1 4
## [4,] 1 5
## [5,] 1 6
## [6,] 1 7
## [7,] 1 8
## [8,] 1 9
## [9,] 1 10
## [10,] 1 11
## [11,] 1 12
## [12,] 1 13
## [13,] 1 14
## [14,] 1 15
## [15,] 1 16
## [16,] 1 17
## [17,] 1 18
## [18,] 1 19
## [19,] 1 20
## [20,] 1 21
## [21,] 1 22
## [22,] 1 23
## [23,] 1 24
## [24,] 1 25
## [25,] 1 26
## [26,] 1 27
## [27,] 1 28
## [28,] 1 29
## [29,] 1 30
## [30,] 1 31
## [31,] 1 32
## [32,] 1 33
## [33,] 1 34
## [34,] 1 35
## [35,] 1 36
## [36,] 1 37
## [37,] 1 38
## [38,] 1 39
## [39,] 1 40
## [40,] 1 41
## [41,] 1 42
## [42,] 1 43
## [43,] 1 44
## [44,] 1 45
## [45,] 1 46
## [46,] 1 47
## [47,] 1 48
## [48,] 1 49
## [49,] 1 50
## [50,] 1 51
## [51,] 1 52
## [52,] 1 53
## [53,] 1 54
## [54,] 1 55
## [55,] 1 56
## [56,] 1 57
## [57,] 1 58
## [58,] 1 59
## [59,] 1 60
## [60,] 1 61
## [61,] 1 62
## [62,] 1 63
## [63,] 1 64
## [64,] 1 65
## [65,] 1 66
## [66,] 1 67
## [67,] 1 68
## [68,] 1 69
## [69,] 1 70
## [70,] 1 71
## [71,] 1 72
## [72,] 1 73
## [73,] 1 74
## [74,] 1 75
## [75,] 1 76
## [76,] 1 77
## [77,] 1 78
## [78,] 1 79
## [79,] 1 80
## [80,] 1 81
## [81,] 1 82
## [82,] 83 3
## [83,] 83 57
## [84,] 2 1
## [85,] 2 3
## [86,] 2 4
## [87,] 2 7
## [88,] 2 8
## [89,] 2 11
## [90,] 2 14
## [91,] 2 16
## [92,] 3 1
## [93,] 3 83
## [94,] 3 2
## [95,] 3 5
## [96,] 3 11
## [97,] 3 12
## [98,] 3 14
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## [100,] 3 22
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## [116,] 5 41
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## [120,] 6 1
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## [124,] 7 11
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## [131,] 8 1
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## [135,] 8 9
## [136,] 8 11
## [137,] 8 56
## [138,] 8 57
## [139,] 8 66
## [140,] 8 79
## [141,] 9 1
## [142,] 9 11
## [143,] 9 17
## [144,] 9 56
## [145,] 9 57
## [146,] 11 1
## [147,] 11 83
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## [191,] 16 1
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## [465,] 34 56
## [466,] 34 57
## [467,] 34 66
## [468,] 34 67
## [469,] 34 79
## [470,] 35 23
## [471,] 35 29
## [472,] 35 31
## [473,] 35 38
## [474,] 35 46
## [475,] 35 56
## [476,] 35 57
## [477,] 35 62
## [478,] 35 66
## [479,] 35 84
## [480,] 36 2
## [481,] 36 3
## [482,] 36 21
## [483,] 36 23
## [484,] 36 27
## [485,] 36 30
## [486,] 36 31
## [487,] 36 37
## [488,] 36 38
## [489,] 37 1
## [490,] 37 3
## [491,] 37 23
## [492,] 37 28
## [493,] 37 33
## [494,] 37 36
## [495,] 37 38
## [496,] 37 41
## [497,] 37 42
## [498,] 37 44
## [499,] 37 57
## [500,] 38 31
## [501,] 38 56
## [502,] 40 1
## [503,] 40 20
## [504,] 40 47
## [505,] 40 57
## [506,] 43 47
## [507,] 43 56
## [508,] 44 21
## [509,] 44 23
## [510,] 44 30
## [511,] 44 37
## [512,] 44 41
## [513,] 45 1
## [514,] 45 23
## [515,] 45 36
## [516,] 45 37
## [517,] 45 47
## [518,] 45 48
## [519,] 45 49
## [520,] 45 50
## [521,] 45 51
## [522,] 45 52
## [523,] 45 53
## [524,] 45 55
## [525,] 45 56
## [526,] 45 57
## [527,] 45 58
## [528,] 45 59
## [529,] 45 60
## [530,] 45 61
## [531,] 45 63
## [532,] 45 64
## [533,] 45 65
## [534,] 45 67
## [535,] 46 1
## [536,] 46 3
## [537,] 46 14
## [538,] 46 23
## [539,] 46 30
## [540,] 46 32
## [541,] 46 33
## [542,] 46 35
## [543,] 46 38
## [544,] 46 50
## [545,] 46 51
## [546,] 46 54
## [547,] 46 55
## [548,] 46 56
## [549,] 46 57
## [550,] 46 60
## [551,] 46 63
## [552,] 46 64
## [553,] 46 65
## [554,] 46 66
## [555,] 46 67
## [556,] 46 71
## [557,] 47 1
## [558,] 47 3
## [559,] 47 11
## [560,] 47 12
## [561,] 47 14
## [562,] 47 17
## [563,] 47 20
## [564,] 47 21
## [565,] 47 24
## [566,] 47 26
## [567,] 47 27
## [568,] 47 30
## [569,] 47 31
## [570,] 47 32
## [571,] 47 38
## [572,] 47 44
## [573,] 47 46
## [574,] 47 47
## [575,] 47 48
## [576,] 47 53
## [577,] 47 54
## [578,] 47 56
## [579,] 47 57
## [580,] 47 60
## [581,] 47 61
## [582,] 47 63
## [583,] 47 64
## [584,] 47 65
## [585,] 47 67
## [586,] 47 68
## [587,] 47 79
## [588,] 49 1
## [589,] 49 46
## [590,] 49 48
## [591,] 49 50
## [592,] 49 51
## [593,] 49 57
## [594,] 50 1
## [595,] 50 3
## [596,] 50 11
## [597,] 50 13
## [598,] 50 45
## [599,] 50 46
## [600,] 50 47
## [601,] 50 49
## [602,] 50 51
## [603,] 50 52
## [604,] 50 53
## [605,] 50 55
## [606,] 50 56
## [607,] 50 57
## [608,] 50 58
## [609,] 50 60
## [610,] 50 63
## [611,] 51 45
## [612,] 51 49
## [613,] 51 50
## [614,] 51 57
## [615,] 51 58
## [616,] 52 56
## [617,] 52 57
## [618,] 54 1
## [619,] 54 57
## [620,] 54 65
## [621,] 54 67
## [622,] 55 57
## [623,] 57 1
## [624,] 57 2
## [625,] 57 3
## [626,] 57 11
## [627,] 57 12
## [628,] 57 19
## [629,] 57 20
## [630,] 57 23
## [631,] 57 28
## [632,] 57 29
## [633,] 57 32
## [634,] 57 34
## [635,] 57 37
## [636,] 57 47
## [637,] 57 53
## [638,] 57 56
## [639,] 57 60
## [640,] 57 61
## [641,] 57 62
## [642,] 57 65
## [643,] 57 67
## [644,] 57 71
## [645,] 57 76
## [646,] 57 79
## [647,] 59 57
## [648,] 60 49
## [649,] 60 57
## [650,] 60 65
## [651,] 61 1
## [652,] 61 57
## [653,] 61 63
## [654,] 61 64
## [655,] 63 3
## [656,] 63 17
## [657,] 63 23
## [658,] 63 26
## [659,] 63 27
## [660,] 63 29
## [661,] 63 31
## [662,] 63 32
## [663,] 63 35
## [664,] 63 36
## [665,] 63 38
## [666,] 63 42
## [667,] 63 43
## [668,] 63 46
## [669,] 63 47
## [670,] 63 54
## [671,] 63 55
## [672,] 63 56
## [673,] 63 57
## [674,] 63 61
## [675,] 63 63
## [676,] 63 64
## [677,] 63 66
## [678,] 63 71
## [679,] 63 74
## [680,] 63 79
## [681,] 64 14
## [682,] 64 16
## [683,] 64 20
## [684,] 64 21
## [685,] 64 27
## [686,] 64 38
## [687,] 64 47
## [688,] 64 52
## [689,] 64 53
## [690,] 64 54
## [691,] 64 57
## [692,] 64 58
## [693,] 64 59
## [694,] 64 65
## [695,] 64 66
## [696,] 65 3
## [697,] 65 47
## [698,] 65 56
## [699,] 65 57
## [700,] 66 1
## [701,] 66 3
## [702,] 66 6
## [703,] 66 17
## [704,] 66 19
## [705,] 66 26
## [706,] 66 27
## [707,] 66 28
## [708,] 66 33
## [709,] 66 34
## [710,] 66 47
## [711,] 66 54
## [712,] 66 56
## [713,] 66 57
## [714,] 66 63
## [715,] 66 64
## [716,] 66 67
## [717,] 66 71
## [718,] 66 79
## [719,] 66 82
## [720,] 67 1
## [721,] 67 6
## [722,] 67 20
## [723,] 67 24
## [724,] 67 30
## [725,] 67 47
## [726,] 67 57
## [727,] 67 73
## [728,] 68 23
## [729,] 68 28
## [730,] 68 41
## [731,] 68 47
## [732,] 68 54
## [733,] 68 57
## [734,] 68 74
## [735,] 68 81
## [736,] 70 21
## [737,] 70 23
## [738,] 70 26
## [739,] 70 27
## [740,] 70 28
## [741,] 70 30
## [742,] 70 45
## [743,] 70 56
## [744,] 70 72
## [745,] 70 73
## [746,] 70 80
## [747,] 70 81
## [748,] 71 3
## [749,] 71 5
## [750,] 71 6
## [751,] 71 7
## [752,] 71 8
## [753,] 71 14
## [754,] 71 15
## [755,] 71 21
## [756,] 71 47
## [757,] 71 51
## [758,] 71 56
## [759,] 71 61
## [760,] 71 66
## [761,] 71 70
## [762,] 71 82
## [763,] 72 70
## [764,] 72 73
## [765,] 72 75
## [766,] 72 80
## [767,] 73 57
## [768,] 74 61
## [769,] 74 63
## [770,] 74 68
## [771,] 74 70
## [772,] 75 1
## [773,] 75 41
## [774,] 75 54
## [775,] 75 57
## [776,] 75 61
## [777,] 76 57
## [778,] 77 57
## [779,] 79 21
## [780,] 79 26
## [781,] 79 27
## [782,] 79 30
## [783,] 79 33
## [784,] 79 34
## [785,] 79 42
## [786,] 79 47
## [787,] 79 54
## [788,] 79 57
## [789,] 79 62
## [790,] 79 63
## [791,] 79 64
## [792,] 79 66
## [793,] 79 67
## [794,] 79 71
## [795,] 79 75
## [796,] 81 2
## [797,] 81 3
## [798,] 81 6
## [799,] 81 9
## [800,] 81 11
## [801,] 81 17
## [802,] 81 20
## [803,] 81 21
## [804,] 81 22
## [805,] 81 26
## [806,] 81 28
## [807,] 81 41
## [808,] 82 57
## [809,] 82 66
## [810,] 82 71
## [811,] 85 24
## [812,] 85 35
## [813,] 85 42
## [814,] 85 57
## [815,] 85 61
## [816,] 85 70
## [817,] 85 71
## [818,] 85 74
## [819,] 85 75
## [820,] 85 79
## [821,] 85 81
## [822,] 86 45
## [823,] 86 47
## [824,] 86 49
## [825,] 86 50
## [826,] 84 1
## [827,] 84 2
## [828,] 84 3
## [829,] 84 16
## [830,] 84 17
## [831,] 84 20
## [832,] 84 23
## [833,] 84 26
## [834,] 84 30
## [835,] 84 33
## [836,] 84 34
## [837,] 84 35
## [838,] 84 42
## [839,] 84 57
## [840,] 84 61
## [841,] 84 63
## [842,] 84 64
## [843,] 84 71
## [844,] 84 72
## [845,] 84 74
## [846,] 84 79
## [847,] 1 83
## [848,] 83 1
## [849,] 2 83
## [850,] 2 2
## [851,] 2 5
## [852,] 2 6
## [853,] 2 9
## [854,] 2 10
## [855,] 2 12
## [856,] 2 13
## [857,] 2 15
## [858,] 2 17
## [859,] 2 18
## [860,] 5 57
## [861,] 7 20
## [862,] 7 57
## [863,] 8 16
## [864,] 8 17
## [865,] 8 18
## [866,] 8 20
## [867,] 8 21
## [868,] 8 25
## [869,] 8 27
## [870,] 8 36
## [871,] 8 40
## [872,] 8 54
## [873,] 8 61
## [874,] 8 71
## [875,] 9 37
## [876,] 10 10
## [877,] 13 6
## [878,] 13 7
## [879,] 13 14
## [880,] 17 6
## [881,] 17 9
## [882,] 17 11
## [883,] 17 23
## [884,] 18 8
## [885,] 18 16
## [886,] 19 21
## [887,] 19 23
## [888,] 19 25
## [889,] 19 26
## [890,] 19 31
## [891,] 19 36
## [892,] 19 40
## [893,] 20 3
## [894,] 20 14
## [895,] 20 16
## [896,] 20 17
## [897,] 20 24
## [898,] 20 26
## [899,] 20 34
## [900,] 20 70
## [901,] 23 11
## [902,] 23 13
## [903,] 23 14
## [904,] 23 15
## [905,] 23 21
## [906,] 23 26
## [907,] 23 27
## [908,] 23 29
## [909,] 23 44
## [910,] 23 52
## [911,] 25 6
## [912,] 25 21
## [913,] 27 69
## [914,] 27 70
## [915,] 27 71
## [916,] 27 72
## [917,] 27 77
## [918,] 27 81
## [919,] 28 30
## [920,] 28 32
## [921,] 28 68
## [922,] 28 70
## [923,] 28 71
## [924,] 28 73
## [925,] 28 75
## [926,] 28 76
## [927,] 29 12
## [928,] 29 17
## [929,] 29 20
## [930,] 29 21
## [931,] 29 30
## [932,] 29 32
## [933,] 29 36
## [934,] 29 47
## [935,] 29 57
## [936,] 29 63
## [937,] 31 1
## [938,] 31 2
## [939,] 31 3
## [940,] 31 17
## [941,] 31 23
## [942,] 33 2
## [943,] 33 35
## [944,] 33 37
## [945,] 33 79
## [946,] 34 3
## [947,] 34 4
## [948,] 34 6
## [949,] 34 23
## [950,] 34 29
## [951,] 34 54
## [952,] 34 55
## [953,] 34 61
## [954,] 35 1
## [955,] 35 21
## [956,] 35 30
## [957,] 35 32
## [958,] 35 42
## [959,] 35 44
## [960,] 35 47
## [961,] 35 61
## [962,] 37 9
## [963,] 37 20
## [964,] 37 63
## [965,] 37 64
## [966,] 37 65
## [967,] 38 28
## [968,] 38 33
## [969,] 38 34
## [970,] 38 36
## [971,] 38 41
## [972,] 40 6
## [973,] 40 21
## [974,] 40 25
## [975,] 40 28
## [976,] 40 53
## [977,] 41 2
## [978,] 41 3
## [979,] 41 28
## [980,] 41 29
## [981,] 41 30
## [982,] 41 31
## [983,] 41 32
## [984,] 41 33
## [985,] 41 34
## [986,] 41 35
## [987,] 41 36
## [988,] 41 37
## [989,] 41 38
## [990,] 41 42
## [991,] 41 44
## [992,] 41 47
## [993,] 41 61
## [994,] 41 63
## [995,] 41 76
## [996,] 43 3
## [997,] 43 17
## [998,] 43 20
## [999,] 43 23
## [1000,] 43 25
## [1001,] 43 27
## [1002,] 43 30
## [1003,] 43 37
## [1004,] 43 38
## [1005,] 43 53
## [1006,] 43 57
## [1007,] 46 45
## [1008,] 47 23
## [1009,] 47 25
## [1010,] 47 28
## [1011,] 47 29
## [1012,] 47 34
## [1013,] 47 35
## [1014,] 47 41
## [1015,] 47 43
## [1016,] 52 45
## [1017,] 53 20
## [1018,] 53 25
## [1019,] 53 31
## [1020,] 53 40
## [1021,] 53 47
## [1022,] 53 56
## [1023,] 55 1
## [1024,] 55 50
## [1025,] 55 63
## [1026,] 57 17
## [1027,] 57 21
## [1028,] 57 38
## [1029,] 57 42
## [1030,] 57 63
## [1031,] 61 11
## [1032,] 61 14
## [1033,] 61 38
## [1034,] 61 60
## [1035,] 61 67
## [1036,] 63 1
## [1037,] 63 14
## [1038,] 63 48
## [1039,] 64 1
## [1040,] 64 3
## [1041,] 64 55
## [1042,] 64 61
## [1043,] 64 63
## [1044,] 69 23
## [1045,] 69 74
## [1046,] 69 78
## [1047,] 70 31
## [1048,] 70 43
## [1049,] 70 47
## [1050,] 70 57
## [1051,] 70 68
## [1052,] 70 77
## [1053,] 71 26
## [1054,] 71 28
## [1055,] 71 42
## [1056,] 71 79
## [1057,] 72 72
## [1058,] 72 74
## [1059,] 73 72
## [1060,] 73 75
## [1061,] 73 81
## [1062,] 74 35
## [1063,] 74 41
## [1064,] 74 56
## [1065,] 74 66
## [1066,] 74 73
## [1067,] 74 75
## [1068,] 74 79
## [1069,] 75 70
## [1070,] 76 20
## [1071,] 76 31
## [1072,] 76 41
## [1073,] 78 75
## [1074,] 79 8
## [1075,] 85 38
## [1076,] 85 63
## [1077,] 86 27
## [1078,] 86 36
## [1079,] 86 70
## [1080,] 86 80
## [1081,] 84 8
## [1082,] 84 9
## [1083,] 84 12
## [1084,] 84 21
## [1085,] 84 29
## [1086,] 84 31
## [1087,] 84 38
## [1088,] 84 46
## [1089,] 84 47
## [1090,] 84 54
## [1091,] 84 56
## [1092,] 84 66
## [1093,] 84 68
summary(n_fi)
## Network attributes:
## vertices = 86
## directed = TRUE
## hyper = FALSE
## loops = TRUE
## multiple = FALSE
## bipartite = FALSE
## total edges = 1093
## missing edges = 0
## non-missing edges = 1093
## density = 0.1477826
##
## Vertex attributes:
##
## b:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 11.25 20.50 25.42 33.00 121.00
##
## g:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 1 10 101 102 105 109 113 114 115 118
## 1 1 1 1 1 1 1 1 1 1
##
## In.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 11.00 12.71 17.00 54.00
##
## label:
## character valued attribute
## attribute summary:
##
## 86
##
## Out.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.25 8.50 12.71 19.00 82.00
##
## r:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## size:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 10 10 10 10 10
## vertex.names:
## character valued attribute
## 86 valid vertex names
##
## x:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -588.79 -269.83 31.12 14.95 283.65 549.75
##
## y:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -523.540 -295.536 -50.040 6.173 313.836 581.694
##
## Edge attributes:
##
## Edge.Label:
## character valued attribute
## attribute summary:
## F I
## 274 819
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 10249 10250 10253 10255 10256 10261 10264 10269 10271 10274
## 1 1 1 1 1 1 1 1 1 1
##
## weight:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.483 5.000 12.000
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 2
## [2,] 1 3
## [3,] 1 4
## [4,] 1 5
## [5,] 1 6
## [6,] 1 7
## [7,] 1 8
## [8,] 1 9
## [9,] 1 10
## [10,] 1 11
## [11,] 1 12
## [12,] 1 13
## [13,] 1 14
## [14,] 1 15
## [15,] 1 16
## [16,] 1 17
## [17,] 1 18
## [18,] 1 19
## [19,] 1 20
## [20,] 1 21
## [21,] 1 22
## [22,] 1 23
## [23,] 1 24
## [24,] 1 25
## [25,] 1 26
## [26,] 1 27
## [27,] 1 28
## [28,] 1 29
## [29,] 1 30
## [30,] 1 31
## [31,] 1 32
## [32,] 1 33
## [33,] 1 34
## [34,] 1 35
## [35,] 1 36
## [36,] 1 37
## [37,] 1 38
## [38,] 1 39
## [39,] 1 40
## [40,] 1 41
## [41,] 1 42
## [42,] 1 43
## [43,] 1 44
## [44,] 1 45
## [45,] 1 46
## [46,] 1 47
## [47,] 1 48
## [48,] 1 49
## [49,] 1 50
## [50,] 1 51
## [51,] 1 52
## [52,] 1 53
## [53,] 1 54
## [54,] 1 55
## [55,] 1 56
## [56,] 1 57
## [57,] 1 58
## [58,] 1 59
## [59,] 1 60
## [60,] 1 61
## [61,] 1 62
## [62,] 1 63
## [63,] 1 64
## [64,] 1 65
## [65,] 1 66
## [66,] 1 67
## [67,] 1 68
## [68,] 1 69
## [69,] 1 70
## [70,] 1 71
## [71,] 1 72
## [72,] 1 73
## [73,] 1 74
## [74,] 1 75
## [75,] 1 76
## [76,] 1 77
## [77,] 1 78
## [78,] 1 79
## [79,] 1 80
## [80,] 1 81
## [81,] 1 82
## [82,] 83 3
## [83,] 83 57
## [84,] 2 1
## [85,] 2 3
## [86,] 2 4
## [87,] 2 7
## [88,] 2 8
## [89,] 2 11
## [90,] 2 14
## [91,] 2 16
## [92,] 3 1
## [93,] 3 83
## [94,] 3 2
## [95,] 3 5
## [96,] 3 11
## [97,] 3 12
## [98,] 3 14
## [99,] 3 17
## [100,] 3 22
## [101,] 3 23
## [102,] 3 24
## [103,] 3 28
## [104,] 3 29
## [105,] 3 31
## [106,] 3 33
## [107,] 3 36
## [108,] 3 37
## [109,] 3 38
## [110,] 3 57
## [111,] 3 63
## [112,] 3 65
## [113,] 4 11
## [114,] 5 3
## [115,] 5 10
## [116,] 5 41
## [117,] 5 54
## [118,] 5 56
## [119,] 5 71
## [120,] 6 1
## [121,] 6 11
## [122,] 6 14
## [123,] 7 7
## [124,] 7 11
## [125,] 7 14
## [126,] 7 19
## [127,] 7 21
## [128,] 7 23
## [129,] 7 25
## [130,] 7 31
## [131,] 8 1
## [132,] 8 2
## [133,] 8 3
## [134,] 8 6
## [135,] 8 9
## [136,] 8 11
## [137,] 8 56
## [138,] 8 57
## [139,] 8 66
## [140,] 8 79
## [141,] 9 1
## [142,] 9 11
## [143,] 9 17
## [144,] 9 56
## [145,] 9 57
## [146,] 11 1
## [147,] 11 83
## [148,] 11 3
## [149,] 11 4
## [150,] 11 5
## [151,] 11 6
## [152,] 11 7
## [153,] 11 8
## [154,] 11 9
## [155,] 11 12
## [156,] 11 13
## [157,] 11 14
## [158,] 11 16
## [159,] 11 18
## [160,] 11 22
## [161,] 11 24
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## [1049,] 70 47
## [1050,] 70 57
## [1051,] 70 68
## [1052,] 70 77
## [1053,] 71 26
## [1054,] 71 28
## [1055,] 71 42
## [1056,] 71 79
## [1057,] 72 72
## [1058,] 72 74
## [1059,] 73 72
## [1060,] 73 75
## [1061,] 73 81
## [1062,] 74 35
## [1063,] 74 41
## [1064,] 74 56
## [1065,] 74 66
## [1066,] 74 73
## [1067,] 74 75
## [1068,] 74 79
## [1069,] 75 70
## [1070,] 76 20
## [1071,] 76 31
## [1072,] 76 41
## [1073,] 78 75
## [1074,] 79 8
## [1075,] 85 38
## [1076,] 85 63
## [1077,] 86 27
## [1078,] 86 36
## [1079,] 86 70
## [1080,] 86 80
## [1081,] 84 8
## [1082,] 84 9
## [1083,] 84 12
## [1084,] 84 21
## [1085,] 84 29
## [1086,] 84 31
## [1087,] 84 38
## [1088,] 84 46
## [1089,] 84 47
## [1090,] 84 54
## [1091,] 84 56
## [1092,] 84 66
## [1093,] 84 68
summary(n_fi)
## Network attributes:
## vertices = 86
## directed = TRUE
## hyper = FALSE
## loops = TRUE
## multiple = FALSE
## bipartite = FALSE
## total edges = 1093
## missing edges = 0
## non-missing edges = 1093
## density = 0.1477826
##
## Vertex attributes:
##
## b:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 11.25 20.50 25.42 33.00 121.00
##
## g:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 1 10 101 102 105 109 113 114 115 118
## 1 1 1 1 1 1 1 1 1 1
##
## In.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 11.00 12.71 17.00 54.00
##
## label:
## character valued attribute
## attribute summary:
##
## 86
##
## Out.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.25 8.50 12.71 19.00 82.00
##
## r:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## size:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 10 10 10 10 10
## vertex.names:
## character valued attribute
## 86 valid vertex names
##
## x:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -588.79 -269.83 31.12 14.95 283.65 549.75
##
## y:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -523.540 -295.536 -50.040 6.173 313.836 581.694
##
## Edge attributes:
##
## Edge.Label:
## character valued attribute
## attribute summary:
## F I
## 274 819
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 10249 10250 10253 10255 10256 10261 10264 10269 10271 10274
## 1 1 1 1 1 1 1 1 1 1
##
## weight:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.483 5.000 12.000
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 2
## [2,] 1 3
## [3,] 1 4
## [4,] 1 5
## [5,] 1 6
## [6,] 1 7
## [7,] 1 8
## [8,] 1 9
## [9,] 1 10
## [10,] 1 11
## [11,] 1 12
## [12,] 1 13
## [13,] 1 14
## [14,] 1 15
## [15,] 1 16
## [16,] 1 17
## [17,] 1 18
## [18,] 1 19
## [19,] 1 20
## [20,] 1 21
## [21,] 1 22
## [22,] 1 23
## [23,] 1 24
## [24,] 1 25
## [25,] 1 26
## [26,] 1 27
## [27,] 1 28
## [28,] 1 29
## [29,] 1 30
## [30,] 1 31
## [31,] 1 32
## [32,] 1 33
## [33,] 1 34
## [34,] 1 35
## [35,] 1 36
## [36,] 1 37
## [37,] 1 38
## [38,] 1 39
## [39,] 1 40
## [40,] 1 41
## [41,] 1 42
## [42,] 1 43
## [43,] 1 44
## [44,] 1 45
## [45,] 1 46
## [46,] 1 47
## [47,] 1 48
## [48,] 1 49
## [49,] 1 50
## [50,] 1 51
## [51,] 1 52
## [52,] 1 53
## [53,] 1 54
## [54,] 1 55
## [55,] 1 56
## [56,] 1 57
## [57,] 1 58
## [58,] 1 59
## [59,] 1 60
## [60,] 1 61
## [61,] 1 62
## [62,] 1 63
## [63,] 1 64
## [64,] 1 65
## [65,] 1 66
## [66,] 1 67
## [67,] 1 68
## [68,] 1 69
## [69,] 1 70
## [70,] 1 71
## [71,] 1 72
## [72,] 1 73
## [73,] 1 74
## [74,] 1 75
## [75,] 1 76
## [76,] 1 77
## [77,] 1 78
## [78,] 1 79
## [79,] 1 80
## [80,] 1 81
## [81,] 1 82
## [82,] 83 3
## [83,] 83 57
## [84,] 2 1
## [85,] 2 3
## [86,] 2 4
## [87,] 2 7
## [88,] 2 8
## [89,] 2 11
## [90,] 2 14
## [91,] 2 16
## [92,] 3 1
## [93,] 3 83
## [94,] 3 2
## [95,] 3 5
## [96,] 3 11
## [97,] 3 12
## [98,] 3 14
## [99,] 3 17
## [100,] 3 22
## [101,] 3 23
## [102,] 3 24
## [103,] 3 28
## [104,] 3 29
## [105,] 3 31
## [106,] 3 33
## [107,] 3 36
## [108,] 3 37
## [109,] 3 38
## [110,] 3 57
## [111,] 3 63
## [112,] 3 65
## [113,] 4 11
## [114,] 5 3
## [115,] 5 10
## [116,] 5 41
## [117,] 5 54
## [118,] 5 56
## [119,] 5 71
## [120,] 6 1
## [121,] 6 11
## [122,] 6 14
## [123,] 7 7
## [124,] 7 11
## [125,] 7 14
## [126,] 7 19
## [127,] 7 21
## [128,] 7 23
## [129,] 7 25
## [130,] 7 31
## [131,] 8 1
## [132,] 8 2
## [133,] 8 3
## [134,] 8 6
## [135,] 8 9
## [136,] 8 11
## [137,] 8 56
## [138,] 8 57
## [139,] 8 66
## [140,] 8 79
## [141,] 9 1
## [142,] 9 11
## [143,] 9 17
## [144,] 9 56
## [145,] 9 57
## [146,] 11 1
## [147,] 11 83
## [148,] 11 3
## [149,] 11 4
## [150,] 11 5
## [151,] 11 6
## [152,] 11 7
## [153,] 11 8
## [154,] 11 9
## [155,] 11 12
## [156,] 11 13
## [157,] 11 14
## [158,] 11 16
## [159,] 11 18
## [160,] 11 22
## [161,] 11 24
## [162,] 11 38
## [163,] 11 48
## [164,] 11 50
## [165,] 11 52
## [166,] 11 53
## [167,] 11 55
## [168,] 11 56
## [169,] 11 57
## [170,] 11 58
## [171,] 11 59
## [172,] 11 65
## [173,] 11 67
## [174,] 12 1
## [175,] 12 2
## [176,] 12 3
## [177,] 12 5
## [178,] 12 6
## [179,] 12 8
## [180,] 12 9
## [181,] 12 10
## [182,] 12 11
## [183,] 12 16
## [184,] 12 17
## [185,] 12 29
## [186,] 14 23
## [187,] 14 38
## [188,] 14 56
## [189,] 14 57
## [190,] 14 58
## [191,] 16 1
## [192,] 16 6
## [193,] 16 8
## [194,] 16 11
## [195,] 16 12
## [196,] 16 16
## [197,] 16 17
## [198,] 16 23
## [199,] 16 56
## [200,] 16 57
## [201,] 16 64
## [202,] 17 1
## [203,] 17 2
## [204,] 17 3
## [205,] 17 12
## [206,] 17 20
## [207,] 17 26
## [208,] 17 29
## [209,] 17 56
## [210,] 17 63
## [211,] 17 66
## [212,] 18 56
## [213,] 18 57
## [214,] 19 20
## [215,] 19 33
## [216,] 19 38
## [217,] 19 56
## [218,] 19 57
## [219,] 19 66
## [220,] 20 1
## [221,] 20 6
## [222,] 20 7
## [223,] 20 8
## [224,] 20 9
## [225,] 20 11
## [226,] 20 19
## [227,] 20 20
## [228,] 20 21
## [229,] 20 23
## [230,] 20 25
## [231,] 20 27
## [232,] 20 28
## [233,] 20 30
## [234,] 20 31
## [235,] 20 32
## [236,] 20 33
## [237,] 20 35
## [238,] 20 36
## [239,] 20 37
## [240,] 20 38
## [241,] 20 40
## [242,] 20 41
## [243,] 20 43
## [244,] 20 45
## [245,] 20 47
## [246,] 20 51
## [247,] 20 52
## [248,] 20 53
## [249,] 20 54
## [250,] 20 55
## [251,] 20 56
## [252,] 20 57
## [253,] 20 58
## [254,] 20 59
## [255,] 20 61
## [256,] 20 62
## [257,] 20 63
## [258,] 20 64
## [259,] 20 65
## [260,] 20 66
## [261,] 20 67
## [262,] 20 71
## [263,] 20 79
## [264,] 21 1
## [265,] 21 2
## [266,] 21 3
## [267,] 21 11
## [268,] 21 14
## [269,] 21 19
## [270,] 21 20
## [271,] 21 23
## [272,] 21 24
## [273,] 21 26
## [274,] 21 27
## [275,] 21 28
## [276,] 21 29
## [277,] 21 30
## [278,] 21 31
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## [280,] 21 33
## [281,] 21 34
## [282,] 21 35
## [283,] 21 36
## [284,] 21 38
## [285,] 21 41
## [286,] 21 43
## [287,] 21 44
## [288,] 21 47
## [289,] 21 49
## [290,] 21 52
## [291,] 21 53
## [292,] 21 55
## [293,] 21 56
## [294,] 21 57
## [295,] 21 58
## [296,] 21 59
## [297,] 21 62
## [298,] 21 63
## [299,] 21 67
## [300,] 21 70
## [301,] 21 71
## [302,] 21 76
## [303,] 21 79
## [304,] 23 1
## [305,] 23 3
## [306,] 23 20
## [307,] 23 22
## [308,] 23 24
## [309,] 23 28
## [310,] 23 30
## [311,] 23 32
## [312,] 23 35
## [313,] 23 37
## [314,] 23 38
## [315,] 23 41
## [316,] 23 53
## [317,] 23 54
## [318,] 23 55
## [319,] 23 56
## [320,] 23 57
## [321,] 23 58
## [322,] 23 59
## [323,] 23 63
## [324,] 24 1
## [325,] 24 83
## [326,] 24 2
## [327,] 24 3
## [328,] 24 4
## [329,] 24 5
## [330,] 24 6
## [331,] 24 7
## [332,] 24 8
## [333,] 24 9
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## [335,] 24 11
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## [338,] 24 14
## [339,] 24 15
## [340,] 24 16
## [341,] 24 17
## [342,] 24 18
## [343,] 24 19
## [344,] 24 20
## [345,] 24 21
## [346,] 24 22
## [347,] 24 23
## [348,] 24 25
## [349,] 24 26
## [350,] 24 27
## [351,] 24 38
## [352,] 24 45
## [353,] 24 47
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## [358,] 24 53
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## [361,] 24 57
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## [364,] 24 61
## [365,] 24 63
## [366,] 24 67
## [367,] 25 1
## [368,] 25 20
## [369,] 25 25
## [370,] 25 28
## [371,] 25 47
## [372,] 25 53
## [373,] 25 57
## [374,] 26 1
## [375,] 26 21
## [376,] 26 23
## [377,] 26 33
## [378,] 26 41
## [379,] 26 79
## [380,] 27 1
## [381,] 27 2
## [382,] 27 3
## [383,] 27 20
## [384,] 27 21
## [385,] 27 28
## [386,] 27 30
## [387,] 27 33
## [388,] 27 51
## [389,] 27 56
## [390,] 27 57
## [391,] 27 63
## [392,] 27 64
## [393,] 27 66
## [394,] 27 79
## [395,] 28 3
## [396,] 28 20
## [397,] 28 21
## [398,] 28 23
## [399,] 28 26
## [400,] 28 31
## [401,] 28 34
## [402,] 28 38
## [403,] 28 40
## [404,] 28 41
## [405,] 28 57
## [406,] 28 66
## [407,] 28 79
## [408,] 29 1
## [409,] 29 3
## [410,] 29 14
## [411,] 29 23
## [412,] 29 31
## [413,] 31 21
## [414,] 31 26
## [415,] 31 27
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## [425,] 31 41
## [426,] 31 42
## [427,] 31 56
## [428,] 31 57
## [429,] 31 63
## [430,] 31 64
## [431,] 32 20
## [432,] 32 23
## [433,] 32 29
## [434,] 32 30
## [435,] 32 63
## [436,] 33 1
## [437,] 33 3
## [438,] 33 17
## [439,] 33 19
## [440,] 33 20
## [441,] 33 21
## [442,] 33 23
## [443,] 33 25
## [444,] 33 27
## [445,] 33 28
## [446,] 33 29
## [447,] 33 30
## [448,] 33 31
## [449,] 33 32
## [450,] 33 36
## [451,] 33 38
## [452,] 33 40
## [453,] 33 41
## [454,] 33 44
## [455,] 33 57
## [456,] 33 61
## [457,] 33 66
## [458,] 33 71
## [459,] 34 1
## [460,] 34 20
## [461,] 34 21
## [462,] 34 24
## [463,] 34 28
## [464,] 34 47
## [465,] 34 56
## [466,] 34 57
## [467,] 34 66
## [468,] 34 67
## [469,] 34 79
## [470,] 35 23
## [471,] 35 29
## [472,] 35 31
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## [474,] 35 46
## [475,] 35 56
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## [477,] 35 62
## [478,] 35 66
## [479,] 35 84
## [480,] 36 2
## [481,] 36 3
## [482,] 36 21
## [483,] 36 23
## [484,] 36 27
## [485,] 36 30
## [486,] 36 31
## [487,] 36 37
## [488,] 36 38
## [489,] 37 1
## [490,] 37 3
## [491,] 37 23
## [492,] 37 28
## [493,] 37 33
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## [495,] 37 38
## [496,] 37 41
## [497,] 37 42
## [498,] 37 44
## [499,] 37 57
## [500,] 38 31
## [501,] 38 56
## [502,] 40 1
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## [504,] 40 47
## [505,] 40 57
## [506,] 43 47
## [507,] 43 56
## [508,] 44 21
## [509,] 44 23
## [510,] 44 30
## [511,] 44 37
## [512,] 44 41
## [513,] 45 1
## [514,] 45 23
## [515,] 45 36
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## [517,] 45 47
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## [535,] 46 1
## [536,] 46 3
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## [539,] 46 30
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## [557,] 47 1
## [558,] 47 3
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## [568,] 47 30
## [569,] 47 31
## [570,] 47 32
## [571,] 47 38
## [572,] 47 44
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## [574,] 47 47
## [575,] 47 48
## [576,] 47 53
## [577,] 47 54
## [578,] 47 56
## [579,] 47 57
## [580,] 47 60
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## [584,] 47 65
## [585,] 47 67
## [586,] 47 68
## [587,] 47 79
## [588,] 49 1
## [589,] 49 46
## [590,] 49 48
## [591,] 49 50
## [592,] 49 51
## [593,] 49 57
## [594,] 50 1
## [595,] 50 3
## [596,] 50 11
## [597,] 50 13
## [598,] 50 45
## [599,] 50 46
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## [601,] 50 49
## [602,] 50 51
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## [610,] 50 63
## [611,] 51 45
## [612,] 51 49
## [613,] 51 50
## [614,] 51 57
## [615,] 51 58
## [616,] 52 56
## [617,] 52 57
## [618,] 54 1
## [619,] 54 57
## [620,] 54 65
## [621,] 54 67
## [622,] 55 57
## [623,] 57 1
## [624,] 57 2
## [625,] 57 3
## [626,] 57 11
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## [628,] 57 19
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## [630,] 57 23
## [631,] 57 28
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## [698,] 65 56
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## [736,] 70 21
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## [758,] 71 56
## [759,] 71 61
## [760,] 71 66
## [761,] 71 70
## [762,] 71 82
## [763,] 72 70
## [764,] 72 73
## [765,] 72 75
## [766,] 72 80
## [767,] 73 57
## [768,] 74 61
## [769,] 74 63
## [770,] 74 68
## [771,] 74 70
## [772,] 75 1
## [773,] 75 41
## [774,] 75 54
## [775,] 75 57
## [776,] 75 61
## [777,] 76 57
## [778,] 77 57
## [779,] 79 21
## [780,] 79 26
## [781,] 79 27
## [782,] 79 30
## [783,] 79 33
## [784,] 79 34
## [785,] 79 42
## [786,] 79 47
## [787,] 79 54
## [788,] 79 57
## [789,] 79 62
## [790,] 79 63
## [791,] 79 64
## [792,] 79 66
## [793,] 79 67
## [794,] 79 71
## [795,] 79 75
## [796,] 81 2
## [797,] 81 3
## [798,] 81 6
## [799,] 81 9
## [800,] 81 11
## [801,] 81 17
## [802,] 81 20
## [803,] 81 21
## [804,] 81 22
## [805,] 81 26
## [806,] 81 28
## [807,] 81 41
## [808,] 82 57
## [809,] 82 66
## [810,] 82 71
## [811,] 85 24
## [812,] 85 35
## [813,] 85 42
## [814,] 85 57
## [815,] 85 61
## [816,] 85 70
## [817,] 85 71
## [818,] 85 74
## [819,] 85 75
## [820,] 85 79
## [821,] 85 81
## [822,] 86 45
## [823,] 86 47
## [824,] 86 49
## [825,] 86 50
## [826,] 84 1
## [827,] 84 2
## [828,] 84 3
## [829,] 84 16
## [830,] 84 17
## [831,] 84 20
## [832,] 84 23
## [833,] 84 26
## [834,] 84 30
## [835,] 84 33
## [836,] 84 34
## [837,] 84 35
## [838,] 84 42
## [839,] 84 57
## [840,] 84 61
## [841,] 84 63
## [842,] 84 64
## [843,] 84 71
## [844,] 84 72
## [845,] 84 74
## [846,] 84 79
## [847,] 1 83
## [848,] 83 1
## [849,] 2 83
## [850,] 2 2
## [851,] 2 5
## [852,] 2 6
## [853,] 2 9
## [854,] 2 10
## [855,] 2 12
## [856,] 2 13
## [857,] 2 15
## [858,] 2 17
## [859,] 2 18
## [860,] 5 57
## [861,] 7 20
## [862,] 7 57
## [863,] 8 16
## [864,] 8 17
## [865,] 8 18
## [866,] 8 20
## [867,] 8 21
## [868,] 8 25
## [869,] 8 27
## [870,] 8 36
## [871,] 8 40
## [872,] 8 54
## [873,] 8 61
## [874,] 8 71
## [875,] 9 37
## [876,] 10 10
## [877,] 13 6
## [878,] 13 7
## [879,] 13 14
## [880,] 17 6
## [881,] 17 9
## [882,] 17 11
## [883,] 17 23
## [884,] 18 8
## [885,] 18 16
## [886,] 19 21
## [887,] 19 23
## [888,] 19 25
## [889,] 19 26
## [890,] 19 31
## [891,] 19 36
## [892,] 19 40
## [893,] 20 3
## [894,] 20 14
## [895,] 20 16
## [896,] 20 17
## [897,] 20 24
## [898,] 20 26
## [899,] 20 34
## [900,] 20 70
## [901,] 23 11
## [902,] 23 13
## [903,] 23 14
## [904,] 23 15
## [905,] 23 21
## [906,] 23 26
## [907,] 23 27
## [908,] 23 29
## [909,] 23 44
## [910,] 23 52
## [911,] 25 6
## [912,] 25 21
## [913,] 27 69
## [914,] 27 70
## [915,] 27 71
## [916,] 27 72
## [917,] 27 77
## [918,] 27 81
## [919,] 28 30
## [920,] 28 32
## [921,] 28 68
## [922,] 28 70
## [923,] 28 71
## [924,] 28 73
## [925,] 28 75
## [926,] 28 76
## [927,] 29 12
## [928,] 29 17
## [929,] 29 20
## [930,] 29 21
## [931,] 29 30
## [932,] 29 32
## [933,] 29 36
## [934,] 29 47
## [935,] 29 57
## [936,] 29 63
## [937,] 31 1
## [938,] 31 2
## [939,] 31 3
## [940,] 31 17
## [941,] 31 23
## [942,] 33 2
## [943,] 33 35
## [944,] 33 37
## [945,] 33 79
## [946,] 34 3
## [947,] 34 4
## [948,] 34 6
## [949,] 34 23
## [950,] 34 29
## [951,] 34 54
## [952,] 34 55
## [953,] 34 61
## [954,] 35 1
## [955,] 35 21
## [956,] 35 30
## [957,] 35 32
## [958,] 35 42
## [959,] 35 44
## [960,] 35 47
## [961,] 35 61
## [962,] 37 9
## [963,] 37 20
## [964,] 37 63
## [965,] 37 64
## [966,] 37 65
## [967,] 38 28
## [968,] 38 33
## [969,] 38 34
## [970,] 38 36
## [971,] 38 41
## [972,] 40 6
## [973,] 40 21
## [974,] 40 25
## [975,] 40 28
## [976,] 40 53
## [977,] 41 2
## [978,] 41 3
## [979,] 41 28
## [980,] 41 29
## [981,] 41 30
## [982,] 41 31
## [983,] 41 32
## [984,] 41 33
## [985,] 41 34
## [986,] 41 35
## [987,] 41 36
## [988,] 41 37
## [989,] 41 38
## [990,] 41 42
## [991,] 41 44
## [992,] 41 47
## [993,] 41 61
## [994,] 41 63
## [995,] 41 76
## [996,] 43 3
## [997,] 43 17
## [998,] 43 20
## [999,] 43 23
## [1000,] 43 25
## [1001,] 43 27
## [1002,] 43 30
## [1003,] 43 37
## [1004,] 43 38
## [1005,] 43 53
## [1006,] 43 57
## [1007,] 46 45
## [1008,] 47 23
## [1009,] 47 25
## [1010,] 47 28
## [1011,] 47 29
## [1012,] 47 34
## [1013,] 47 35
## [1014,] 47 41
## [1015,] 47 43
## [1016,] 52 45
## [1017,] 53 20
## [1018,] 53 25
## [1019,] 53 31
## [1020,] 53 40
## [1021,] 53 47
## [1022,] 53 56
## [1023,] 55 1
## [1024,] 55 50
## [1025,] 55 63
## [1026,] 57 17
## [1027,] 57 21
## [1028,] 57 38
## [1029,] 57 42
## [1030,] 57 63
## [1031,] 61 11
## [1032,] 61 14
## [1033,] 61 38
## [1034,] 61 60
## [1035,] 61 67
## [1036,] 63 1
## [1037,] 63 14
## [1038,] 63 48
## [1039,] 64 1
## [1040,] 64 3
## [1041,] 64 55
## [1042,] 64 61
## [1043,] 64 63
## [1044,] 69 23
## [1045,] 69 74
## [1046,] 69 78
## [1047,] 70 31
## [1048,] 70 43
## [1049,] 70 47
## [1050,] 70 57
## [1051,] 70 68
## [1052,] 70 77
## [1053,] 71 26
## [1054,] 71 28
## [1055,] 71 42
## [1056,] 71 79
## [1057,] 72 72
## [1058,] 72 74
## [1059,] 73 72
## [1060,] 73 75
## [1061,] 73 81
## [1062,] 74 35
## [1063,] 74 41
## [1064,] 74 56
## [1065,] 74 66
## [1066,] 74 73
## [1067,] 74 75
## [1068,] 74 79
## [1069,] 75 70
## [1070,] 76 20
## [1071,] 76 31
## [1072,] 76 41
## [1073,] 78 75
## [1074,] 79 8
## [1075,] 85 38
## [1076,] 85 63
## [1077,] 86 27
## [1078,] 86 36
## [1079,] 86 70
## [1080,] 86 80
## [1081,] 84 8
## [1082,] 84 9
## [1083,] 84 12
## [1084,] 84 21
## [1085,] 84 29
## [1086,] 84 31
## [1087,] 84 38
## [1088,] 84 46
## [1089,] 84 47
## [1090,] 84 54
## [1091,] 84 56
## [1092,] 84 66
## [1093,] 84 68
summary(n_fi)
## Network attributes:
## vertices = 86
## directed = TRUE
## hyper = FALSE
## loops = TRUE
## multiple = FALSE
## bipartite = FALSE
## total edges = 1093
## missing edges = 0
## non-missing edges = 1093
## density = 0.1477826
##
## Vertex attributes:
##
## b:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 11.25 20.50 25.42 33.00 121.00
##
## g:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 1 10 101 102 105 109 113 114 115 118
## 1 1 1 1 1 1 1 1 1 1
##
## In.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 11.00 12.71 17.00 54.00
##
## label:
## character valued attribute
## attribute summary:
##
## 86
##
## Out.Degree:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.25 8.50 12.71 19.00 82.00
##
## r:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## size:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 10 10 10 10 10
## vertex.names:
## character valued attribute
## 86 valid vertex names
##
## x:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -588.79 -269.83 31.12 14.95 283.65 549.75
##
## y:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -523.540 -295.536 -50.040 6.173 313.836 581.694
##
## Edge attributes:
##
## Edge.Label:
## character valued attribute
## attribute summary:
## F I
## 274 819
##
## id:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## 10249 10250 10253 10255 10256 10261 10264 10269 10271 10274
## 1 1 1 1 1 1 1 1 1 1
##
## weight:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.483 5.000 12.000
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 2
## [2,] 1 3
## [3,] 1 4
## [4,] 1 5
## [5,] 1 6
## [6,] 1 7
## [7,] 1 8
## [8,] 1 9
## [9,] 1 10
## [10,] 1 11
## [11,] 1 12
## [12,] 1 13
## [13,] 1 14
## [14,] 1 15
## [15,] 1 16
## [16,] 1 17
## [17,] 1 18
## [18,] 1 19
## [19,] 1 20
## [20,] 1 21
## [21,] 1 22
## [22,] 1 23
## [23,] 1 24
## [24,] 1 25
## [25,] 1 26
## [26,] 1 27
## [27,] 1 28
## [28,] 1 29
## [29,] 1 30
## [30,] 1 31
## [31,] 1 32
## [32,] 1 33
## [33,] 1 34
## [34,] 1 35
## [35,] 1 36
## [36,] 1 37
## [37,] 1 38
## [38,] 1 39
## [39,] 1 40
## [40,] 1 41
## [41,] 1 42
## [42,] 1 43
## [43,] 1 44
## [44,] 1 45
## [45,] 1 46
## [46,] 1 47
## [47,] 1 48
## [48,] 1 49
## [49,] 1 50
## [50,] 1 51
## [51,] 1 52
## [52,] 1 53
## [53,] 1 54
## [54,] 1 55
## [55,] 1 56
## [56,] 1 57
## [57,] 1 58
## [58,] 1 59
## [59,] 1 60
## [60,] 1 61
## [61,] 1 62
## [62,] 1 63
## [63,] 1 64
## [64,] 1 65
## [65,] 1 66
## [66,] 1 67
## [67,] 1 68
## [68,] 1 69
## [69,] 1 70
## [70,] 1 71
## [71,] 1 72
## [72,] 1 73
## [73,] 1 74
## [74,] 1 75
## [75,] 1 76
## [76,] 1 77
## [77,] 1 78
## [78,] 1 79
## [79,] 1 80
## [80,] 1 81
## [81,] 1 82
## [82,] 83 3
## [83,] 83 57
## [84,] 2 1
## [85,] 2 3
## [86,] 2 4
## [87,] 2 7
## [88,] 2 8
## [89,] 2 11
## [90,] 2 14
## [91,] 2 16
## [92,] 3 1
## [93,] 3 83
## [94,] 3 2
## [95,] 3 5
## [96,] 3 11
## [97,] 3 12
## [98,] 3 14
## [99,] 3 17
## [100,] 3 22
## [101,] 3 23
## [102,] 3 24
## [103,] 3 28
## [104,] 3 29
## [105,] 3 31
## [106,] 3 33
## [107,] 3 36
## [108,] 3 37
## [109,] 3 38
## [110,] 3 57
## [111,] 3 63
## [112,] 3 65
## [113,] 4 11
## [114,] 5 3
## [115,] 5 10
## [116,] 5 41
## [117,] 5 54
## [118,] 5 56
## [119,] 5 71
## [120,] 6 1
## [121,] 6 11
## [122,] 6 14
## [123,] 7 7
## [124,] 7 11
## [125,] 7 14
## [126,] 7 19
## [127,] 7 21
## [128,] 7 23
## [129,] 7 25
## [130,] 7 31
## [131,] 8 1
## [132,] 8 2
## [133,] 8 3
## [134,] 8 6
## [135,] 8 9
## [136,] 8 11
## [137,] 8 56
## [138,] 8 57
## [139,] 8 66
## [140,] 8 79
## [141,] 9 1
## [142,] 9 11
## [143,] 9 17
## [144,] 9 56
## [145,] 9 57
## [146,] 11 1
## [147,] 11 83
## [148,] 11 3
## [149,] 11 4
## [150,] 11 5
## [151,] 11 6
## [152,] 11 7
## [153,] 11 8
## [154,] 11 9
## [155,] 11 12
## [156,] 11 13
## [157,] 11 14
## [158,] 11 16
## [159,] 11 18
## [160,] 11 22
## [161,] 11 24
## [162,] 11 38
## [163,] 11 48
## [164,] 11 50
## [165,] 11 52
## [166,] 11 53
## [167,] 11 55
## [168,] 11 56
## [169,] 11 57
## [170,] 11 58
## [171,] 11 59
## [172,] 11 65
## [173,] 11 67
## [174,] 12 1
## [175,] 12 2
## [176,] 12 3
## [177,] 12 5
## [178,] 12 6
## [179,] 12 8
## [180,] 12 9
## [181,] 12 10
## [182,] 12 11
## [183,] 12 16
## [184,] 12 17
## [185,] 12 29
## [186,] 14 23
## [187,] 14 38
## [188,] 14 56
## [189,] 14 57
## [190,] 14 58
## [191,] 16 1
## [192,] 16 6
## [193,] 16 8
## [194,] 16 11
## [195,] 16 12
## [196,] 16 16
## [197,] 16 17
## [198,] 16 23
## [199,] 16 56
## [200,] 16 57
## [201,] 16 64
## [202,] 17 1
## [203,] 17 2
## [204,] 17 3
## [205,] 17 12
## [206,] 17 20
## [207,] 17 26
## [208,] 17 29
## [209,] 17 56
## [210,] 17 63
## [211,] 17 66
## [212,] 18 56
## [213,] 18 57
## [214,] 19 20
## [215,] 19 33
## [216,] 19 38
## [217,] 19 56
## [218,] 19 57
## [219,] 19 66
## [220,] 20 1
## [221,] 20 6
## [222,] 20 7
## [223,] 20 8
## [224,] 20 9
## [225,] 20 11
## [226,] 20 19
## [227,] 20 20
## [228,] 20 21
## [229,] 20 23
## [230,] 20 25
## [231,] 20 27
## [232,] 20 28
## [233,] 20 30
## [234,] 20 31
## [235,] 20 32
## [236,] 20 33
## [237,] 20 35
## [238,] 20 36
## [239,] 20 37
## [240,] 20 38
## [241,] 20 40
## [242,] 20 41
## [243,] 20 43
## [244,] 20 45
## [245,] 20 47
## [246,] 20 51
## [247,] 20 52
## [248,] 20 53
## [249,] 20 54
## [250,] 20 55
## [251,] 20 56
## [252,] 20 57
## [253,] 20 58
## [254,] 20 59
## [255,] 20 61
## [256,] 20 62
## [257,] 20 63
## [258,] 20 64
## [259,] 20 65
## [260,] 20 66
## [261,] 20 67
## [262,] 20 71
## [263,] 20 79
## [264,] 21 1
## [265,] 21 2
## [266,] 21 3
## [267,] 21 11
## [268,] 21 14
## [269,] 21 19
## [270,] 21 20
## [271,] 21 23
## [272,] 21 24
## [273,] 21 26
## [274,] 21 27
## [275,] 21 28
## [276,] 21 29
## [277,] 21 30
## [278,] 21 31
## [279,] 21 32
## [280,] 21 33
## [281,] 21 34
## [282,] 21 35
## [283,] 21 36
## [284,] 21 38
## [285,] 21 41
## [286,] 21 43
## [287,] 21 44
## [288,] 21 47
## [289,] 21 49
## [290,] 21 52
## [291,] 21 53
## [292,] 21 55
## [293,] 21 56
## [294,] 21 57
## [295,] 21 58
## [296,] 21 59
## [297,] 21 62
## [298,] 21 63
## [299,] 21 67
## [300,] 21 70
## [301,] 21 71
## [302,] 21 76
## [303,] 21 79
## [304,] 23 1
## [305,] 23 3
## [306,] 23 20
## [307,] 23 22
## [308,] 23 24
## [309,] 23 28
## [310,] 23 30
## [311,] 23 32
## [312,] 23 35
## [313,] 23 37
## [314,] 23 38
## [315,] 23 41
## [316,] 23 53
## [317,] 23 54
## [318,] 23 55
## [319,] 23 56
## [320,] 23 57
## [321,] 23 58
## [322,] 23 59
## [323,] 23 63
## [324,] 24 1
## [325,] 24 83
## [326,] 24 2
## [327,] 24 3
## [328,] 24 4
## [329,] 24 5
## [330,] 24 6
## [331,] 24 7
## [332,] 24 8
## [333,] 24 9
## [334,] 24 10
## [335,] 24 11
## [336,] 24 12
## [337,] 24 13
## [338,] 24 14
## [339,] 24 15
## [340,] 24 16
## [341,] 24 17
## [342,] 24 18
## [343,] 24 19
## [344,] 24 20
## [345,] 24 21
## [346,] 24 22
## [347,] 24 23
## [348,] 24 25
## [349,] 24 26
## [350,] 24 27
## [351,] 24 38
## [352,] 24 45
## [353,] 24 47
## [354,] 24 49
## [355,] 24 50
## [356,] 24 51
## [357,] 24 52
## [358,] 24 53
## [359,] 24 54
## [360,] 24 55
## [361,] 24 57
## [362,] 24 58
## [363,] 24 59
## [364,] 24 61
## [365,] 24 63
## [366,] 24 67
## [367,] 25 1
## [368,] 25 20
## [369,] 25 25
## [370,] 25 28
## [371,] 25 47
## [372,] 25 53
## [373,] 25 57
## [374,] 26 1
## [375,] 26 21
## [376,] 26 23
## [377,] 26 33
## [378,] 26 41
## [379,] 26 79
## [380,] 27 1
## [381,] 27 2
## [382,] 27 3
## [383,] 27 20
## [384,] 27 21
## [385,] 27 28
## [386,] 27 30
## [387,] 27 33
## [388,] 27 51
## [389,] 27 56
## [390,] 27 57
## [391,] 27 63
## [392,] 27 64
## [393,] 27 66
## [394,] 27 79
## [395,] 28 3
## [396,] 28 20
## [397,] 28 21
## [398,] 28 23
## [399,] 28 26
## [400,] 28 31
## [401,] 28 34
## [402,] 28 38
## [403,] 28 40
## [404,] 28 41
## [405,] 28 57
## [406,] 28 66
## [407,] 28 79
## [408,] 29 1
## [409,] 29 3
## [410,] 29 14
## [411,] 29 23
## [412,] 29 31
## [413,] 31 21
## [414,] 31 26
## [415,] 31 27
## [416,] 31 28
## [417,] 31 29
## [418,] 31 30
## [419,] 31 32
## [420,] 31 33
## [421,] 31 35
## [422,] 31 36
## [423,] 31 37
## [424,] 31 38
## [425,] 31 41
## [426,] 31 42
## [427,] 31 56
## [428,] 31 57
## [429,] 31 63
## [430,] 31 64
## [431,] 32 20
## [432,] 32 23
## [433,] 32 29
## [434,] 32 30
## [435,] 32 63
## [436,] 33 1
## [437,] 33 3
## [438,] 33 17
## [439,] 33 19
## [440,] 33 20
## [441,] 33 21
## [442,] 33 23
## [443,] 33 25
## [444,] 33 27
## [445,] 33 28
## [446,] 33 29
## [447,] 33 30
## [448,] 33 31
## [449,] 33 32
## [450,] 33 36
## [451,] 33 38
## [452,] 33 40
## [453,] 33 41
## [454,] 33 44
## [455,] 33 57
## [456,] 33 61
## [457,] 33 66
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node_ifi<- cbind(V(i_fi),V(i_fi)$id,V(i_fi)$label)
node_ifi
## [,1] [,2] [,3]
## [1,] "1" "1" ""
## [2,] "2" "3" ""
## [3,] "3" "4" ""
## [4,] "4" "7" ""
## [5,] "5" "9" ""
## [6,] "6" "10" ""
## [7,] "7" "15" ""
## [8,] "8" "18" ""
## [9,] "9" "23" ""
## [10,] "10" "25" ""
## [11,] "11" "28" ""
## [12,] "12" "31" ""
## [13,] "13" "32" ""
## [14,] "14" "34" ""
## [15,] "15" "37" ""
## [16,] "16" "38" ""
## [17,] "17" "39" ""
## [18,] "18" "40" ""
## [19,] "19" "42" ""
## [20,] "20" "44" ""
## [21,] "21" "46" ""
## [22,] "22" "50" ""
## [23,] "23" "51" ""
## [24,] "24" "53" ""
## [25,] "25" "58" ""
## [26,] "26" "61" ""
## [27,] "27" "62" ""
## [28,] "28" "63" ""
## [29,] "29" "65" ""
## [30,] "30" "66" ""
## [31,] "31" "67" ""
## [32,] "32" "68" ""
## [33,] "33" "70" ""
## [34,] "34" "75" ""
## [35,] "35" "76" ""
## [36,] "36" "77" ""
## [37,] "37" "78" ""
## [38,] "38" "80" ""
## [39,] "39" "81" ""
## [40,] "40" "83" ""
## [41,] "41" "85" ""
## [42,] "42" "86" ""
## [43,] "43" "88" ""
## [44,] "44" "89" ""
## [45,] "45" "90" ""
## [46,] "46" "91" ""
## [47,] "47" "92" ""
## [48,] "48" "95" ""
## [49,] "49" "97" ""
## [50,] "50" "99" ""
## [51,] "51" "101" ""
## [52,] "52" "102" ""
## [53,] "53" "105" ""
## [54,] "54" "109" ""
## [55,] "55" "113" ""
## [56,] "56" "114" ""
## [57,] "57" "115" ""
## [58,] "58" "118" ""
## [59,] "59" "120" ""
## [60,] "60" "122" ""
## [61,] "61" "124" ""
## [62,] "62" "125" ""
## [63,] "63" "126" ""
## [64,] "64" "127" ""
## [65,] "65" "128" ""
## [66,] "66" "130" ""
## [67,] "67" "131" ""
## [68,] "68" "135" ""
## [69,] "69" "136" ""
## [70,] "70" "137" ""
## [71,] "71" "138" ""
## [72,] "72" "139" ""
## [73,] "73" "140" ""
## [74,] "74" "141" ""
## [75,] "75" "142" ""
## [76,] "76" "145" ""
## [77,] "77" "146" ""
## [78,] "78" "148" ""
## [79,] "79" "150" ""
## [80,] "80" "152" ""
## [81,] "81" "153" ""
## [82,] "82" "155" ""
## [83,] "83" "2" ""
## [84,] "84" "159" ""
## [85,] "85" "157" ""
## [86,] "86" "158" ""
# FI
# 기술통계
## Network Summary
length(V(i_fi))
## [1] 86
length(E(i_fi))
## [1] 1093
## Degree distribution
n_fi_dd <-degree(n_fi)
hist(n_fi_dd, col="blue", xlab = "Degree", ylab="Frequence", main="Degree Distribution")
# Network Strucutre
## Network Level
### Density
graph.density(i_fi)
## [1] 0.1495212
### Reciprocity: the proportion of dyads which are symmetric
grecip(n_fi, measure="dyadic")
## Mut
## 0.8298222
grecip(n_fi, measure="edgewise")
## Mut
## 0.4261993
### Mutuality: the nuber of complete dyads
mutuality(n_fi)
## Mut
## 231
### Transitivity: Total number of transitivie triads is computed
gtrans(n_fi)
## [1] 0.3983914
## Graph centralization (degree, betweenness, closeness)
centralization.degree(i_fi)$centralization
## [1] 0.5688581
centralization.betweenness(i_fi)$centralization
## [1] 0.2292277
centralization.closeness(i_fi)$centralization
## Warning in centralization.closeness(i_fi): At centrality.c:2784 :closeness
## centrality is not well-defined for disconnected graphs
## [1] 0.1260271
### Average Path Length
average.path.length(i_fi, directed=TRUE)
## [1] 2.009154
### Global Clustering co-effeciency
transitivity(i_fi, type="global")
## [1] 0.4350079
## Node Level
### Centrality
#### Degree
i_fi_degree <- igraph::degree(i_fi)
i_fi_indegree <- igraph::degree(i_fi, mode="in")
i_fi_outdegree <- igraph::degree(i_fi, mode="out")
#### Betweenness
i_fi_betweeness <- igraph::betweenness(i_fi, directed =TRUE)
#### Closeness
i_fi_closeness <- igraph::closeness(i_fi)
## Warning in igraph::closeness(i_fi): At centrality.c:2617 :closeness
## centrality is not well-defined for disconnected graphs
#### Hub-Authority
i_fi_hubscore<-igraph::hub_score(i_fi)$vector
i_fi_authscore<-igraph::authority_score(i_fi)$vector
#### Constraint
i_fi_constraint <- igraph::constraint(i_fi)
fi_centrality<-select(node, Id, Lable)
fi_centrality$degree<-i_fi_degree
fi_centrality$indegree <- i_fi_indegree
fi_centrality$outdegree <- i_fi_outdegree
fi_centrality$between <- i_fi_betweeness
fi_centrality$closeness <- i_fi_closeness
fi_centrality$hub <- i_fi_hubscore
fi_centrality$authority <- i_fi_authscore
fi_centrality$constraint <- i_fi_constraint
describe(fi_centrality)
## Warning in describe(fi_centrality): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning
## Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## vars n mean sd median trimmed mad min max range
## Id 1 86 85.64 46.50 87.00 86.89 60.05 1.00 159.0 158.00
## Lable* 2 86 NaN NA NA NaN NA Inf -Inf -Inf
## degree 3 86 25.42 20.24 20.50 22.36 16.31 1.00 121.0 120.00
## indegree 4 86 12.71 9.24 11.00 11.57 7.41 0.00 54.0 54.00
## outdegree 5 86 12.71 13.51 8.50 10.63 11.12 0.00 82.0 82.00
## between 6 86 125.35 191.43 44.14 80.84 65.45 0.00 765.2 765.20
## closeness 7 86 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.00
## hub 8 86 0.13 0.16 0.09 0.10 0.09 0.00 1.0 1.00
## authority 9 86 0.22 0.14 0.21 0.21 0.13 0.00 1.0 1.00
## constraint 10 86 0.20 0.13 0.17 0.18 0.07 0.06 1.0 0.94
## skew kurtosis se
## Id -0.16 -1.17 5.01
## Lable* NA NA NA
## degree 1.88 5.02 2.18
## indegree 1.60 3.75 1.00
## outdegree 2.14 6.90 1.46
## between 1.89 2.59 20.64
## closeness -1.29 0.72 0.00
## hub 2.81 10.55 0.02
## authority 1.95 8.36 0.02
## constraint 3.17 14.65 0.01
#write.csv(fi_centrality, "../finding/fi_degree.csv")
require(xlsx)
## Loading required package: xlsx
## Error: package or namespace load failed for 'xlsx':
## .onLoad failed in loadNamespace() for 'rJava', details:
## call: dyn.load(file, DLLpath = DLLpath, ...)
## error: unable to load shared object '/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/libs/rJava.so':
## dlopen(/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/libs/rJava.so, 6): Library not loaded: /Library/Java/JavaVirtualMachines/jdk-9.jdk/Contents/Home/lib/server/libjvm.dylib
## Referenced from: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/libs/rJava.so
## Reason: image not found
#write.xlsx(fi_centrality, file = "../finding/fi_centrality.xlsx",
# sheetName = "centrality", append = FALSE)
## Sub graph Level
### Census
as.list(dyad_census(i_fi))
## $mut
## [1] 235
##
## $asym
## [1] 622
##
## $null
## [1] 2798
as.list(triad_census(i_fi))
## [[1]]
## [1] 51809
##
## [[2]]
## [1] 25207
##
## [[3]]
## [1] 7785
##
## [[4]]
## [1] 3520
##
## [[5]]
## [1] 1782
##
## [[6]]
## [1] 2103
##
## [[7]]
## [1] 1690
##
## [[8]]
## [1] 3723
##
## [[9]]
## [1] 989
##
## [[10]]
## [1] 45
##
## [[11]]
## [1] 1139
##
## [[12]]
## [1] 370
##
## [[13]]
## [1] 693
##
## [[14]]
## [1] 373
##
## [[15]]
## [1] 844
##
## [[16]]
## [1] 268
as.vector(dyad_census(i_fi))
## $mut
## [1] 235
##
## $asym
## [1] 622
##
## $null
## [1] 2798
as.vector(triad_census(i_fi))
## [1] 51809 25207 7785 3520 1782 2103 1690 3723 989 45 1139
## [12] 370 693 373 844 268
plot((triad_census(i_fi)),type="o",lty=3, xlab="Subgraph",ylab="Log Frequency")
points((triad_census(i_fi)),type="o",lty=2)
plot(log(triad_census(i_fi)),type="o",lty=3, xlab="Subgraph",ylab="Log Frequency")
points(log(triad_census(i_fi)),type="o",lty=2)
### N-Clique
largest_cliques(i_fi)
## Warning in largest_cliques(i_fi): At cliques.c:1087 :directionality of
## edges is ignored for directed graphs
## [[1]]
## + 12/86 vertices, from fac4930:
## [1] 1 57 20 21 23 47 56 38 63 31 35 84
### Modularaity (Community)
i_fi_community <- cluster_walktrap(i_fi)
modularity(i_fi_community)
## [1] 0.1378577
membership(i_fi_community)
## [1] 3 1 3 1 1 1 1 1 1 1 1 1 1 1 3 1 3 1 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 7 3 3 3 3 3 2 2 3 2 2 2 2 2 2 3 2 3 3 2 2 2 3 3 3 3 3 3 3 6 5 6
## [71] 3 4 4 6 4 3 6 5 3 4 6 3 1 3 6 3
### Ego Centric Analysis
ego_14_34<-make_ego_graph(i_fi, 1, 14)
plot(ego_14_34[[1]])
graph.density(ego_14_34[[1]])
## [1] 0.4466403
centralization.degree(ego_14_34[[1]])$centralization
## [1] 0.4121901
ego_15_37<-make_ego_graph(i_fi, 1, 37)
plot(ego_15_37[[1]])
graph.density(ego_15_37[[1]])
## [1] 0.4631579
centralization.degree(ego_15_37[[1]])$centralization
## [1] 0.3434903
## Additional Analysis
### Small World
graphntrials <- 1000
graphnv <- vcount (i_fi)
graphne <- ecount (i_fi)
graphcl.rg <- numeric (graphntrials)
graphapl.rg <- numeric (graphntrials)
for (i in (1:graphntrials)) {
graphg.rg <- erdos.renyi.game(graphnv, graphne, type="gnm", directed=TRUE)
graphcl.rg[i] <- transitivity(graphg.rg, type="global")
graphapl.rg[i] <- average.path.length (graphg.rg)
}
graphdist_cl.rg <- graphcl.rg
graphdist_apl.rg <- graphapl.rg
summary(graphcl.rg)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2602 0.2729 0.2763 0.2764 0.2796 0.2935
summary(graphapl.rg)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.959 1.973 1.977 1.977 1.981 1.995
transitivity(i_fi, type="global")
## [1] 0.4350079
average.path.length(i_fi, directed=TRUE)
## [1] 2.009154
### Brokerage
i_fi_broker<-brokerage(n_fi, node$소속기관)
summary(i_fi_broker)
## Gould-Fernandez Brokerage Analysis
##
## Global Brokerage Properties
## t E(t) Sd(t) z Pr(>|z|)
## w_I 2675.000 2787.728 212.055 -0.5316 0.5950
## w_O 2832.000 2831.080 227.251 0.0040 0.9968
## b_IO 2902.000 2831.080 169.048 0.4195 0.6748
## b_OI 2690.000 2831.080 169.048 -0.8346 0.4040
## b_O 420.000 394.270 66.203 0.3887 0.6975
## t 11519.000 11675.237 602.270 -0.2594 0.7953
##
## Individual Properties (by Group)
##
## Group ID: 2
## w_I w_O b_IO b_OI b_O t w_I w_O b_IO b_OI
## 1 374 770 592 613 120 2469 25.9245621 33.5757560 30.69516517 31.8488588
## 4 0 1 0 0 0 1 -1.6693921 -1.9364630 -1.82800899 -1.8280090
## 5 4 10 4 8 0 26 -1.3742696 -1.5208454 -1.60825781 -1.3885066
## 7 4 10 5 10 0 29 -1.3742696 -1.5208454 -1.55332002 -1.2786310
## 12 2 31 9 4 0 46 -1.5218309 -0.5510709 -1.33356884 -1.6082578
## 13 2 1 1 1 0 5 -1.5218309 -1.9364630 -1.77307120 -1.7730712
## 14 9 8 4 15 1 37 -1.0053665 -1.6132049 -1.60825781 -1.0039421
## 15 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 20 138 249 204 237 44 872 8.5123343 9.5161122 9.37930102 11.1922482
## 21 53 267 102 225 22 669 2.2409811 10.3473475 3.77564602 10.5329947
## 22 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 24 39 73 31 85 3 231 1.2080523 1.3884782 -0.12493737 2.8417035
## 25 5 10 6 6 0 27 -1.3004890 -1.5208454 -1.49838222 -1.4983822
## 32 2 2 4 6 0 14 -1.5218309 -1.8902833 -1.60825781 -1.4983822
## 46 31 7 18 14 0 70 0.6178073 -1.6593846 -0.83912869 -1.0588799
## 48 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 49 15 0 0 6 0 21 -0.5626827 -1.9826428 -1.82800899 -1.4983822
## 50 34 6 13 19 0 72 0.8391492 -1.7055643 -1.11381767 -0.7841909
## 51 5 2 1 7 0 15 -1.3004890 -1.8902833 -1.77307120 -1.4434444
## 52 2 2 2 0 0 6 -1.5218309 -1.8902833 -1.71813340 -1.8280090
## 53 8 6 11 6 6 37 -1.0791471 -1.7055643 -1.22369325 -1.4983822
## 54 13 0 0 9 0 22 -0.7102440 -1.9826428 -1.82800899 -1.3335688
## 55 4 0 0 4 0 8 -1.3742696 -1.9826428 -1.82800899 -1.6082578
## 56 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 57 196 325 300 254 48 1123 12.7916106 13.0257723 14.65332927 12.1261907
## 58 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 59 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 60 3 0 0 3 0 6 -1.4480502 -1.9826428 -1.82800899 -1.6631956
## 62 0 0 0 0 0 0 -1.6693921 -1.9826428 -1.82800899 -1.8280090
## 63 58 124 107 97 23 409 2.6098842 3.7436448 4.05033499 3.5009570
## 64 29 20 9 68 9 135 0.4702460 -1.0590480 -1.33356884 1.9077610
## 65 3 4 5 2 0 14 -1.4480502 -1.7979238 -1.55332002 -1.7181334
## 66 4 98 32 50 0 184 -1.3742696 2.5429716 -0.06999958 0.9188807
## 67 7 12 17 8 4 48 -1.1529277 -1.4284859 -0.89406649 -1.3885066
## 68 0 11 3 4 0 18 -1.6693921 -1.4746656 -1.66319561 -1.6082578
## 84 0 13 0 6 0 19 -1.6693921 -1.3823062 -1.82800899 -1.4983822
## b_O t
## 1 24.51082568 46.02084875
## 4 -0.76906717 -2.65797785
## 5 -0.76906717 -2.16487790
## 7 -0.76906717 -2.10570591
## 12 -0.76906717 -1.77039795
## 13 -0.76906717 -2.57908186
## 14 -0.55840139 -1.94791393
## 15 -0.76906717 -2.67770185
## 20 8.50022687 14.52162425
## 21 3.86557985 10.51765269
## 22 -0.76906717 -2.67770185
## 24 -0.13706985 1.87854165
## 25 -0.76906717 -2.14515390
## 32 -0.76906717 -2.40156588
## 46 -0.76906717 -1.29702200
## 48 -0.76906717 -2.67770185
## 49 -0.76906717 -2.26349789
## 50 -0.76906717 -1.25757400
## 51 -0.76906717 -2.38184188
## 52 -0.76906717 -2.55935786
## 53 0.49492747 -1.94791393
## 54 -0.76906717 -2.24377389
## 55 -0.76906717 -2.51990986
## 56 -0.76906717 -2.67770185
## 57 9.34288997 19.47234770
## 58 -0.76906717 -2.67770185
## 59 -0.76906717 -2.67770185
## 60 -0.76906717 -2.55935786
## 62 -0.76906717 -2.67770185
## 63 4.07624563 5.38941326
## 64 1.12692479 -0.01496214
## 65 -0.76906717 -2.40156588
## 66 -0.76906717 0.95151375
## 67 0.07359593 -1.73094995
## 68 -0.76906717 -2.32266988
## 84 -0.76906717 -2.30294589
##
## Group ID: 1
## w_I w_O b_IO b_OI b_O t w_I w_O b_IO
## 2 61 23 62 22 0 168 0.947833884 -0.07043497 1.524924403
## 3 134 62 113 99 0 408 4.426184349 2.68915882 4.298370255
## 6 2 5 7 5 1 20 -1.863435670 -1.34409365 -1.466046613
## 8 45 10 43 15 4 117 0.185455700 -0.99029957 0.491679870
## 9 8 3 7 4 0 22 -1.577543851 -1.48561128 -1.466046613
## 10 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 11 49 96 147 47 16 355 0.376050246 5.09495854 6.147334156
## 16 8 8 9 8 0 33 -1.577543851 -1.13181720 -1.357284031
## 17 43 12 34 19 0 108 0.090158427 -0.84878194 0.002248249
## 18 0 1 2 0 0 3 -1.958732943 -1.62712891 -1.737953069
## 19 1 11 1 15 5 33 -1.911084307 -0.91954075 -1.792334361
## 23 140 109 255 78 0 582 4.712076168 6.01482313 12.020513607
## 26 21 2 7 8 0 38 -0.958111576 -1.55637009 -1.466046613
## 27 81 14 48 46 0 189 1.900806614 -0.70726431 0.763586326
## 28 110 16 54 61 8 249 3.282617073 -0.56574668 1.089874074
## 29 22 18 28 16 0 84 -0.910462940 -0.42422905 -0.324039498
## 30 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 31 102 14 46 48 0 210 2.901427981 -0.70726431 0.654823744
## 33 94 11 54 35 7 201 2.520238889 -0.91954075 1.089874074
## 34 18 18 40 7 2 85 -1.101057486 -0.42422905 0.328535997
## 35 23 14 40 12 2 91 -0.862814303 -0.70726431 0.328535997
## 36 41 1 6 5 0 53 -0.005138846 -1.62712891 -1.520427905
## 37 51 1 35 7 0 94 0.471347519 -1.62712891 0.056629541
## 38 44 2 7 40 1 94 0.137807063 -1.55637009 -1.466046613
## 39 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 41 135 4 25 36 3 203 4.473832986 -1.41485246 -0.487183372
## 42 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 43 5 4 3 4 0 16 -1.720489761 -1.41485246 -1.683571778
## 44 5 0 2 1 0 8 -1.720489761 -1.69788772 -1.737953069
## 45 4 46 32 16 4 102 -1.768138397 1.55701778 -0.106514333
## 47 116 165 153 166 22 622 3.568508892 9.97731678 6.473621903
## 69 3 0 0 0 0 3 -1.815787034 -1.69788772 -1.846715652
## 70 88 2 20 11 0 121 2.234347070 -1.55637009 -0.759089828
## 71 45 39 46 41 4 175 0.185455700 1.06170607 0.654823744
## 72 9 0 0 4 0 13 -1.529895215 -1.69788772 -1.846715652
## 73 6 0 2 3 0 11 -1.672841124 -1.69788772 -1.737953069
## 74 11 4 13 13 1 42 -1.434597942 -1.41485246 -1.139758866
## 75 8 0 21 0 0 29 -1.577543851 -1.69788772 -0.704708536
## 76 0 0 2 2 0 4 -1.958732943 -1.69788772 -1.737953069
## 77 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 78 1 0 0 0 0 1 -1.911084307 -1.69788772 -1.846715652
## 79 66 13 47 22 0 148 1.186077067 -0.77802312 0.709205035
## 80 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 81 31 3 9 7 0 50 -0.481625211 -1.48561128 -1.357284031
## 82 0 0 1 0 0 1 -1.958732943 -1.69788772 -1.792334361
## 83 0 0 1 0 0 1 -1.958732943 -1.69788772 -1.792334361
## 85 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## 86 0 0 0 0 0 0 -1.958732943 -1.69788772 -1.846715652
## b_OI b_O t
## 2 -0.65032725 -0.74947045 0.6359298
## 3 3.53703218 -0.74947045 5.3696893
## 6 -1.57480920 -0.47574009 -2.2832219
## 8 -1.03099628 0.34545097 -0.3699941
## 9 -1.62919049 -0.74947045 -2.2437739
## 10 -1.84671565 -0.74947045 -2.6777018
## 11 0.70920504 3.63021524 4.3243174
## 16 -1.41166532 -0.74947045 -2.0268099
## 17 -0.81347112 -0.74947045 -0.5475101
## 18 -1.84671565 -0.74947045 -2.6185299
## 19 -1.03099628 0.61918133 -2.0268099
## 23 2.39502506 -0.74947045 8.8016649
## 26 -1.41166532 -0.74947045 -1.9281899
## 27 0.65482374 -0.74947045 1.0501337
## 28 1.47054311 1.44037239 2.2335736
## 29 -0.97661499 -0.74947045 -1.0208860
## 30 -1.84671565 -0.74947045 -2.6777018
## 31 0.76358633 -0.74947045 1.4643377
## 33 0.05662954 1.16664204 1.2868217
## 34 -1.46604661 -0.20200974 -1.0011620
## 35 -1.19414016 -0.20200974 -0.8828180
## 36 -1.57480920 -0.74947045 -1.6323300
## 37 -1.46604661 -0.74947045 -0.8236461
## 38 0.32853600 -0.47574009 -0.8236461
## 39 -1.84671565 -0.74947045 -2.6777018
## 41 0.11101083 0.07172062 1.3262697
## 42 -1.84671565 -0.74947045 -2.6777018
## 43 -1.62919049 -0.74947045 -2.3621179
## 44 -1.79233436 -0.74947045 -2.5199099
## 45 -0.97661499 0.34545097 -0.6658541
## 47 7.18057869 5.27259737 9.5906248
## 69 -1.84671565 -0.74947045 -2.6185299
## 70 -1.24852145 -0.74947045 -0.2910981
## 71 0.38291729 0.34545097 0.7739978
## 72 -1.62919049 -0.74947045 -2.4212899
## 73 -1.68357178 -0.74947045 -2.4607379
## 74 -1.13975887 -0.47574009 -1.8492939
## 75 -1.84671565 -0.74947045 -2.1057059
## 76 -1.73795307 -0.74947045 -2.5988059
## 77 -1.84671565 -0.74947045 -2.6777018
## 78 -1.84671565 -0.74947045 -2.6579778
## 79 -0.65032725 -0.74947045 0.2414498
## 80 -1.84671565 -0.74947045 -2.6777018
## 81 -1.46604661 -0.74947045 -1.6915020
## 82 -1.84671565 -0.74947045 -2.6579778
## 83 -1.84671565 -0.74947045 -2.6579778
## 85 -1.84671565 -0.74947045 -2.6777018
## 86 -1.84671565 -0.74947045 -2.6777018
##
## Group ID: 3
## w_I w_O b_IO b_OI b_O t w_I w_O b_IO b_OI b_O
## 40 0 12 0 0 9 21 NaN -2.121401 -0.4004967 -0.4004967 -2.2291923
## 61 0 27 0 0 51 78 NaN -1.541282 -0.4004967 -0.4004967 -0.5782798
## t
## 40 -2.263498
## 61 -1.139230
i_fi_broker$raw.nli
## w_I w_O b_IO b_OI b_O t
## 1 374 770 592 613 120 2469
## 2 61 23 62 22 0 168
## 3 134 62 113 99 0 408
## 4 0 1 0 0 0 1
## 5 4 10 4 8 0 26
## 6 2 5 7 5 1 20
## 7 4 10 5 10 0 29
## 8 45 10 43 15 4 117
## 9 8 3 7 4 0 22
## 10 0 0 0 0 0 0
## 11 49 96 147 47 16 355
## 12 2 31 9 4 0 46
## 13 2 1 1 1 0 5
## 14 9 8 4 15 1 37
## 15 0 0 0 0 0 0
## 16 8 8 9 8 0 33
## 17 43 12 34 19 0 108
## 18 0 1 2 0 0 3
## 19 1 11 1 15 5 33
## 20 138 249 204 237 44 872
## 21 53 267 102 225 22 669
## 22 0 0 0 0 0 0
## 23 140 109 255 78 0 582
## 24 39 73 31 85 3 231
## 25 5 10 6 6 0 27
## 26 21 2 7 8 0 38
## 27 81 14 48 46 0 189
## 28 110 16 54 61 8 249
## 29 22 18 28 16 0 84
## 30 0 0 0 0 0 0
## 31 102 14 46 48 0 210
## 32 2 2 4 6 0 14
## 33 94 11 54 35 7 201
## 34 18 18 40 7 2 85
## 35 23 14 40 12 2 91
## 36 41 1 6 5 0 53
## 37 51 1 35 7 0 94
## 38 44 2 7 40 1 94
## 39 0 0 0 0 0 0
## 40 0 12 0 0 9 21
## 41 135 4 25 36 3 203
## 42 0 0 0 0 0 0
## 43 5 4 3 4 0 16
## 44 5 0 2 1 0 8
## 45 4 46 32 16 4 102
## 46 31 7 18 14 0 70
## 47 116 165 153 166 22 622
## 48 0 0 0 0 0 0
## 49 15 0 0 6 0 21
## 50 34 6 13 19 0 72
## 51 5 2 1 7 0 15
## 52 2 2 2 0 0 6
## 53 8 6 11 6 6 37
## 54 13 0 0 9 0 22
## 55 4 0 0 4 0 8
## 56 0 0 0 0 0 0
## 57 196 325 300 254 48 1123
## 58 0 0 0 0 0 0
## 59 0 0 0 0 0 0
## 60 3 0 0 3 0 6
## 61 0 27 0 0 51 78
## 62 0 0 0 0 0 0
## 63 58 124 107 97 23 409
## 64 29 20 9 68 9 135
## 65 3 4 5 2 0 14
## 66 4 98 32 50 0 184
## 67 7 12 17 8 4 48
## 68 0 11 3 4 0 18
## 69 3 0 0 0 0 3
## 70 88 2 20 11 0 121
## 71 45 39 46 41 4 175
## 72 9 0 0 4 0 13
## 73 6 0 2 3 0 11
## 74 11 4 13 13 1 42
## 75 8 0 21 0 0 29
## 76 0 0 2 2 0 4
## 77 0 0 0 0 0 0
## 78 1 0 0 0 0 1
## 79 66 13 47 22 0 148
## 80 0 0 0 0 0 0
## 81 31 3 9 7 0 50
## 82 0 0 1 0 0 1
## 83 0 0 1 0 0 1
## 84 0 13 0 6 0 19
## 85 0 0 0 0 0 0
## 86 0 0 0 0 0 0
brokerrole<-cbind(node$Lable, i_fi_broker$raw.nli)
colnames(brokerrole)<-c("기관명",
"Coordinator role",
"Itinerant broker role",
"Representative role",
"Gatekeeper role",
"Liaison role",
"Total")
brokerrole
## 기관명 Coordinator role
## 1 "은평구평생학습관" "374"
## 2 "MBL문고" "61"
## 3 "은평도서관마을사회적협동조합" "134"
## 4 "구립구산동도서관마을" "0"
## 5 "녹번만화도서관" "4"
## 6 "다섯콩작은도서관" "2"
## 7 "대조꿈나무어린이도서관" "4"
## 8 "북한산 힐스테이트1차 아파트 솔숲문고" "45"
## 9 "산책마을작은도서관" "8"
## 10 "수색한숲도서관" "0"
## 11 "어린이도서관동네북" "49"
## 12 "은평구립도서관" "2"
## 13 "은평어린이영어도서관" "2"
## 14 "은평작은도서관" "9"
## 15 "응암정보도서관" "0"
## 16 "징검다리도서관" "8"
## 17 "책뜰에도서관" "43"
## 18 "초록길도서관" "0"
## 19 "푸른작은도서관" "1"
## 20 "불광도시재생지원센터" "138"
## 21 "은평구마을공동체지원센터" "53"
## 22 "은평구사회적경제허브센터" "0"
## 23 "은평문화원" "140"
## 24 "은평문화재단" "39"
## 25 "은평역사한옥박물관" "5"
## 26 "녹번산골마을" "21"
## 27 "마을무지개" "81"
## 28 "물푸레생태교육센터" "110"
## 29 "반짝반짝사진방" "22"
## 30 "북카페 쿠아레" "0"
## 31 "사단법인은평상상" "102"
## 32 "살림의료복지사회적협동조합" "2"
## 33 "열린사회시민연합은평시민회" "94"
## 34 "우리동네텃밭협동조합" "18"
## 35 "은평교육문화협동조합" "23"
## 36 "은평노동인권센터" "41"
## 37 "은평두레생활협동조합" "51"
## 38 "은평마을예술창작소" "44"
## 39 "은평시민신문" "0"
## 40 "은평씨앗학교" "0"
## 41 "응암산골마을" "135"
## 42 "자바르떼" "0"
## 43 "작공" "5"
## 44 "즐거운반딧불이" "5"
## 45 "풍물패터울림" "4"
## 46 "갈현노인복지관" "31"
## 47 "공립벧엘지역아동센터" "116"
## 48 "녹번종합사회복지관" "0"
## 49 "시립평화로운집" "15"
## 50 "신사노인복지관" "34"
## 51 "역촌노인복지관" "5"
## 52 "응암노인복지관" "2"
## 53 "갈현1동자치회관" "8"
## 54 "녹번동자치회관" "13"
## 55 "서부교육지원청 평생교육건강과" "4"
## 56 "역촌동자치회관" "0"
## 57 "은평구자원봉사센터" "196"
## 58 "은평구청 민관협치과" "0"
## 59 "응암3동자치회관" "0"
## 60 "진관동자치회관" "3"
## 61 "서울기독대학교 평생교육원" "0"
## 62 "서울시은평청소년수련관" "0"
## 63 "서울시평생교육진흥원 서울자유시민대학 은평학습장" "58"
## 64 "신나는애프터센터" "29"
## 65 "은평교육복지센터" "3"
## 66 "은평구건강가정다문화가족지원센터" "4"
## 67 "은평마을방과후지원센터" "7"
## 68 "은평여성인력개발센터" "0"
## 69 "감성붓다" "3"
## 70 "공연예술창작소호밀" "88"
## 71 "금자동이" "45"
## 72 "노는엄마협동조합" "9"
## 73 "대안교육연대" "6"
## 74 "대안에너지기술연구소" "11"
## 75 "도원교육" "8"
## 76 "마을기술센터핸즈" "0"
## 77 "앙코르브라보노협동조합" "0"
## 78 "언더독스" "1"
## 79 "위드쌤" "66"
## 80 "㈜케이로드 넘버원" "0"
## 81 "지식순환협동조합" "31"
## 82 "팝업놀이터" "0"
## 83 "해피어울림협동조합" "0"
## 84 "은평청소년진로직업체험지원센터" "0"
## 85 "주빌리은행" "0"
## 86 "은평혁신교육지원단" "0"
## Itinerant broker role Representative role Gatekeeper role Liaison role
## 1 "770" "592" "613" "120"
## 2 "23" "62" "22" "0"
## 3 "62" "113" "99" "0"
## 4 "1" "0" "0" "0"
## 5 "10" "4" "8" "0"
## 6 "5" "7" "5" "1"
## 7 "10" "5" "10" "0"
## 8 "10" "43" "15" "4"
## 9 "3" "7" "4" "0"
## 10 "0" "0" "0" "0"
## 11 "96" "147" "47" "16"
## 12 "31" "9" "4" "0"
## 13 "1" "1" "1" "0"
## 14 "8" "4" "15" "1"
## 15 "0" "0" "0" "0"
## 16 "8" "9" "8" "0"
## 17 "12" "34" "19" "0"
## 18 "1" "2" "0" "0"
## 19 "11" "1" "15" "5"
## 20 "249" "204" "237" "44"
## 21 "267" "102" "225" "22"
## 22 "0" "0" "0" "0"
## 23 "109" "255" "78" "0"
## 24 "73" "31" "85" "3"
## 25 "10" "6" "6" "0"
## 26 "2" "7" "8" "0"
## 27 "14" "48" "46" "0"
## 28 "16" "54" "61" "8"
## 29 "18" "28" "16" "0"
## 30 "0" "0" "0" "0"
## 31 "14" "46" "48" "0"
## 32 "2" "4" "6" "0"
## 33 "11" "54" "35" "7"
## 34 "18" "40" "7" "2"
## 35 "14" "40" "12" "2"
## 36 "1" "6" "5" "0"
## 37 "1" "35" "7" "0"
## 38 "2" "7" "40" "1"
## 39 "0" "0" "0" "0"
## 40 "12" "0" "0" "9"
## 41 "4" "25" "36" "3"
## 42 "0" "0" "0" "0"
## 43 "4" "3" "4" "0"
## 44 "0" "2" "1" "0"
## 45 "46" "32" "16" "4"
## 46 "7" "18" "14" "0"
## 47 "165" "153" "166" "22"
## 48 "0" "0" "0" "0"
## 49 "0" "0" "6" "0"
## 50 "6" "13" "19" "0"
## 51 "2" "1" "7" "0"
## 52 "2" "2" "0" "0"
## 53 "6" "11" "6" "6"
## 54 "0" "0" "9" "0"
## 55 "0" "0" "4" "0"
## 56 "0" "0" "0" "0"
## 57 "325" "300" "254" "48"
## 58 "0" "0" "0" "0"
## 59 "0" "0" "0" "0"
## 60 "0" "0" "3" "0"
## 61 "27" "0" "0" "51"
## 62 "0" "0" "0" "0"
## 63 "124" "107" "97" "23"
## 64 "20" "9" "68" "9"
## 65 "4" "5" "2" "0"
## 66 "98" "32" "50" "0"
## 67 "12" "17" "8" "4"
## 68 "11" "3" "4" "0"
## 69 "0" "0" "0" "0"
## 70 "2" "20" "11" "0"
## 71 "39" "46" "41" "4"
## 72 "0" "0" "4" "0"
## 73 "0" "2" "3" "0"
## 74 "4" "13" "13" "1"
## 75 "0" "21" "0" "0"
## 76 "0" "2" "2" "0"
## 77 "0" "0" "0" "0"
## 78 "0" "0" "0" "0"
## 79 "13" "47" "22" "0"
## 80 "0" "0" "0" "0"
## 81 "3" "9" "7" "0"
## 82 "0" "1" "0" "0"
## 83 "0" "1" "0" "0"
## 84 "13" "0" "6" "0"
## 85 "0" "0" "0" "0"
## 86 "0" "0" "0" "0"
## Total
## 1 "2469"
## 2 "168"
## 3 "408"
## 4 "1"
## 5 "26"
## 6 "20"
## 7 "29"
## 8 "117"
## 9 "22"
## 10 "0"
## 11 "355"
## 12 "46"
## 13 "5"
## 14 "37"
## 15 "0"
## 16 "33"
## 17 "108"
## 18 "3"
## 19 "33"
## 20 "872"
## 21 "669"
## 22 "0"
## 23 "582"
## 24 "231"
## 25 "27"
## 26 "38"
## 27 "189"
## 28 "249"
## 29 "84"
## 30 "0"
## 31 "210"
## 32 "14"
## 33 "201"
## 34 "85"
## 35 "91"
## 36 "53"
## 37 "94"
## 38 "94"
## 39 "0"
## 40 "21"
## 41 "203"
## 42 "0"
## 43 "16"
## 44 "8"
## 45 "102"
## 46 "70"
## 47 "622"
## 48 "0"
## 49 "21"
## 50 "72"
## 51 "15"
## 52 "6"
## 53 "37"
## 54 "22"
## 55 "8"
## 56 "0"
## 57 "1123"
## 58 "0"
## 59 "0"
## 60 "6"
## 61 "78"
## 62 "0"
## 63 "409"
## 64 "135"
## 65 "14"
## 66 "184"
## 67 "48"
## 68 "18"
## 69 "3"
## 70 "121"
## 71 "175"
## 72 "13"
## 73 "11"
## 74 "42"
## 75 "29"
## 76 "4"
## 77 "0"
## 78 "1"
## 79 "148"
## 80 "0"
## 81 "50"
## 82 "1"
## 83 "1"
## 84 "19"
## 85 "0"
## 86 "0"
#write.xlsx(brokerrole, file = "../finding/brokerrole.xlsx",
# sheetName = "brokerrole", append = FALSE)
### MDS
gplot(n_fi,label.cex=0.5,label.col="blue",
displaylabels=TRUE, displayisolates=FALSE,mode="mds")
### Blockmodel
ec <- equiv.clust(n_fi,
mode="graph",
plabels=network.vertex.names(n_fi))
plot(ec) # Plot the dendrogram
rect.hclust(ec$cluster, h=60)
bm <- blockmodel(n_fi, ec, h=60)
bm
##
## Network Blockmodel:
##
## Block membership:
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## 1 2 2 3 3 3 3 4 3 3 5 2 3 3 3 3 2 3 3 6 6 3 6 5 3
## 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 2 3 2 2 2 2 2 2 3 2 2 2 2 3 3 2 2 3 2 4 4 6 3 3 4
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## 3 3 3 3 3 3 7 3 3 3 3 2 6 4 3 3 3 3 3 3 3 3 3 3 3
## 76 77 78 79 80 81 82 83 84 85 86
## 2 3 3 3 3 3 3 3 4 3 3
##
## Reduced form blockmodel:
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
## Block 1 Block 2 Block 3 Block 4 Block 5 Block 6
## Block 1 NaN 1.00000000 0.96078431 0.83333333 1.00000000 1.0000000
## Block 2 0.5000000 0.30789474 0.05588235 0.05000000 0.12500000 0.3700000
## Block 3 0.2941176 0.06470588 0.05764706 0.05882353 0.09803922 0.1921569
## Block 4 1.0000000 0.22500000 0.22549020 0.33333333 0.16666667 0.6000000
## Block 5 1.0000000 0.22500000 0.45098039 0.41666667 1.00000000 0.5000000
## Block 6 1.0000000 0.64000000 0.31764706 0.23333333 0.80000000 0.8500000
## Block 7 1.0000000 0.60000000 0.19607843 0.00000000 0.50000000 1.0000000
## Block 7
## Block 1 1.000000
## Block 2 0.400000
## Block 3 0.627451
## Block 4 1.000000
## Block 5 1.000000
## Block 6 1.000000
## Block 7 NaN
plot.sociomatrix(n_fi[bm$order.vector,bm$order.vector],drawlab=FALSE)
bimage <- bm$block.model
bimage
## Block 1 Block 2 Block 3 Block 4 Block 5 Block 6
## Block 1 NaN 1.00000000 0.96078431 0.83333333 1.00000000 1.0000000
## Block 2 0.5000000 0.30789474 0.05588235 0.05000000 0.12500000 0.3700000
## Block 3 0.2941176 0.06470588 0.05764706 0.05882353 0.09803922 0.1921569
## Block 4 1.0000000 0.22500000 0.22549020 0.33333333 0.16666667 0.6000000
## Block 5 1.0000000 0.22500000 0.45098039 0.41666667 1.00000000 0.5000000
## Block 6 1.0000000 0.64000000 0.31764706 0.23333333 0.80000000 0.8500000
## Block 7 1.0000000 0.60000000 0.19607843 0.00000000 0.50000000 1.0000000
## Block 7
## Block 1 1.000000
## Block 2 0.400000
## Block 3 0.627451
## Block 4 1.000000
## Block 5 1.000000
## Block 6 1.000000
## Block 7 NaN
bimage[is.nan(bimage)] <- 1
gplot(bimage, diag=TRUE, edge.lwd=bimage*5,
vertex.cex=sqrt(table(bm$block.membership))/2,
gmode="graph", vertex.sides=50, vertex.col=gray(1-diag(bimage)))
### Clustering / heatmap
netm <- as_adjacency_matrix(i_fi, attr="weight", sparse=F)
colnames(netm) <- node$Lable
rownames(netm) <- node$Lable
palf <- colorRampPalette(c("gold", "dark orange"))
heatmap(netm, Rowv = NA, Colv = NA, col = palf(100),
scale="none", margins=c(10,10) )
