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Load Packages
install.packages("remotes")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)remotes::install_github("knapply/homophily")## Skipping install of 'homophily' from a github remote, the SHA1 (78dc6841) has not changed since last install.
## Use `force = TRUE` to force installationlibrary("homophily")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(ggraph)
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:dplyr':
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## as_data_frame, groups, union## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
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## unionlibrary(tidygraph)##
## Attaching package: 'tidygraph'## The following object is masked from 'package:igraph':
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## groups## The following object is masked from 'package:stats':
##
## filterlibrary(readr)
library(tibble)##
## Attaching package: 'tibble'## The following object is masked from 'package:igraph':
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## as_data_framelibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%--%() masks igraph::%--%()
## ✖ tibble::as_data_frame() masks igraph::as_data_frame(), dplyr::as_data_frame()
## ✖ purrr::compose() masks igraph::compose()
## ✖ tidyr::crossing() masks igraph::crossing()
## ✖ tidygraph::filter() masks dplyr::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::simplify() masks igraph::simplify()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyr)
library(datasets)Load Dataset
#see this website https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZZH3UB}
DLT1_Edgelist <- read_csv("DLT1 Edgelist.csv")## Rows: 2529 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Timestamp, Discussion Title, Discussion Category, Parent Category, ...
## dbl (3): Sender, Receiver, Comment ID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.DLT1_Nodes <- read_csv("DLT1 Nodes.csv")## Rows: 445 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): role1, experience2, grades, location, region, country, group, gend...
## dbl (3): UID, Facilitator, experience
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Isolate variables in Edge List
select(DLT1_Edgelist, Sender, Receiver, Timestamp, `Discussion Category`, `Comment ID`, `Discussion ID`) -> DLT1_Edgelist2Create df without the discussion groups
?read.csv
DLT1_Edgelist2[!grepl('Group', DLT1_Edgelist2$'Discussion Category'),] -> DLT1_EdgeNoDiscCreate df with only the discussion groups
DLT1_EdgeDisc <- anti_join(DLT1_Edgelist2, DLT1_EdgeNoDisc)## Joining with `by = join_by(Sender, Receiver, Timestamp, `Discussion Category`,
## `Comment ID`, `Discussion ID`)`Above df contained a few extra values that I did not want, so I will exclude them.
DLT1_EdgeDisc[!grepl('Self-Assessment', DLT1_EdgeDisc$'Discussion Category'),] -> DLT1_EdgeDisc2Create Network Objects based on the whole network
dlt1_network_grup <- tbl_graph(edges = DLT1_EdgeDisc2,
nodes = DLT1_Nodes,
node_key = "uid",
directed = TRUE)
DLT1_Network_ungrup <- tbl_graph(edges = DLT1_EdgeNoDisc,
nodes = DLT1_Nodes,
node_key = "uid",
directed = TRUE)Graph the networks and determine the number of cliques
plot(dlt1_network_grup)
plot(DLT1_Network_ungrup)
autograph(dlt1_network_grup)
autograph(DLT1_Network_ungrup)
?clique_size_counts
clique_num(dlt1_network_grup)## Warning in clique_num(dlt1_network_grup): At
## core/cliques/maximal_cliques_template.h:268 : Edge directions are ignored for
## maximal clique calculation.## [1] 6clique_num(DLT1_Network_ungrup)## Warning in clique_num(DLT1_Network_ungrup): At
## core/cliques/maximal_cliques_template.h:268 : Edge directions are ignored for
## maximal clique calculation.## [1] 8clique_size_counts(dlt1_network_grup)## Warning in clique_size_hist_impl(graph, min, max, ...): At
## core/cliques/cliquer_wrapper.c:57 : Edge directions are ignored for clique
## calculations.## [1] 445 943 582 133 16 1clique_size_counts(DLT1_Network_ungrup)## Warning in clique_size_hist_impl(graph, min, max, ...): At
## core/cliques/cliquer_wrapper.c:57 : Edge directions are ignored for clique
## calculations.## [1] 445 877 998 672 244 50 8 1The cliques are a jumbled mess, isolated around the professors, so I will create new networks and graph them to see how the network changes
DLT1_EdgeDisc2[!grepl('444', DLT1_EdgeDisc2$'Sender'),] -> DLT1_EdgeDisc_no444
DLT1_EdgeDisc_no444[!grepl('445', DLT1_EdgeDisc_no444$'Sender'),] -> DLT1_EdgeDisc_noProf_Send
DLT1_EdgeDisc_noProf_Send[!grepl('444', DLT1_EdgeDisc_noProf_Send$'Receiver'),] -> DLT1_EdgeDisc_Send_no444
DLT1_EdgeDisc_Send_no444[!grepl('445', DLT1_EdgeDisc_Send_no444$'Receiver'),] -> DLT1_EdgeDisc_noProf
DLT1_EdgeNoDisc[!grepl('444', DLT1_EdgeNoDisc$'Sender'),] -> DLT1_EdgeNoDisc_no444
DLT1_EdgeNoDisc_no444[!grepl('445', DLT1_EdgeNoDisc_no444$'Sender'),] -> DLT1_EdgeNoDisc_noProf_Send
DLT1_EdgeNoDisc_noProf_Send[!grepl('444', DLT1_EdgeNoDisc_noProf_Send$'Receiver'),] -> DLT1_EdgeNoDisc_no444_Send
DLT1_EdgeNoDisc_no444_Send[!grepl('445', DLT1_EdgeNoDisc_no444_Send$'Receiver'),] -> DLT1_EdgeNoDisc_noProf
DLT1_Nodes[!grepl('444', DLT1_Nodes$'UID'),] -> DLT1_Nodes_no444
DLT1_Nodes_no444[!grepl('445', DLT1_Nodes_no444$'UID'),] -> DLT1_Nodes_noProf
DLT1_Networknoprof_ungrup <- tbl_graph(edges = DLT1_EdgeNoDisc_noProf,
nodes = DLT1_Nodes_noProf,
node_key = "uid",
directed = TRUE)
DLT1_networknoprof_grup <- tbl_graph(edges = DLT1_EdgeDisc_noProf,
nodes = DLT1_Nodes_noProf,
node_key = "uid",
directed = TRUE)
plot(DLT1_networknoprof_grup)
plot(DLT1_Networknoprof_ungrup)
autograph(DLT1_networknoprof_grup)
autograph(DLT1_Networknoprof_ungrup)
?clique_size_counts
clique_num(DLT1_networknoprof_grup)## Warning in clique_num(DLT1_networknoprof_grup): At
## core/cliques/maximal_cliques_template.h:268 : Edge directions are ignored for
## maximal clique calculation.## [1] 5clique_num(DLT1_Networknoprof_ungrup)## Warning in clique_num(DLT1_Networknoprof_ungrup): At
## core/cliques/maximal_cliques_template.h:268 : Edge directions are ignored for
## maximal clique calculation.## [1] 6clique_size_counts(DLT1_networknoprof_grup)## Warning in clique_size_hist_impl(graph, min, max, ...): At
## core/cliques/cliquer_wrapper.c:57 : Edge directions are ignored for clique
## calculations.## [1] 443 574 163 18 2clique_size_counts(DLT1_Networknoprof_ungrup)## Warning in clique_size_hist_impl(graph, min, max, ...): At
## core/cliques/cliquer_wrapper.c:57 : Edge directions are ignored for clique
## calculations.## [1] 443 720 547 204 33 1The number of unengaged people rises once the professors and interactions with them are removed. Moreover, the grouped chart clusters into five distinct groups. I will try to separate the clusters and establish n-cliques, those cliques separated by at least n ties.
?connect.neighborhood
plot(connect.neighborhood(DLT1_networknoprof_grup, order = 2))
plot(connect.neighborhood(DLT1_Networknoprof_ungrup, order = 2))
autograph(connect.neighborhood(DLT1_networknoprof_grup, order = 2))
autograph(connect.neighborhood(DLT1_Networknoprof_ungrup, order = 2))
clique_num(connect.neighborhood(DLT1_networknoprof_grup, order = 2))## Warning in clique_num(connect.neighborhood(DLT1_networknoprof_grup, order =
## 2)): At core/cliques/maximal_cliques_template.h:268 : Edge directions are
## ignored for maximal clique calculation.## [1] 27clique_num(connect.neighborhood(DLT1_Networknoprof_ungrup, order = 2))## Warning in clique_num(connect.neighborhood(DLT1_Networknoprof_ungrup, order =
## 2)): At core/cliques/maximal_cliques_template.h:268 : Edge directions are
## ignored for maximal clique calculation.## [1] 48That method did not seem to work, instead tightening the clustering. Nonetheless, one can still see that in the original graph, there are distinct clusters. I will graph those later. For now, I will focus on determining the data metrics for each of the four networks created: grouped and ungrouped networks with and without professors. For sake of ease, I placed a “k” before the text, since the resulting table is long. The following section does much the same thing.
DLT1_networknoprof_grup |> centr_degree() |> as_tibble() |> select(centralization) |> unique()centr_degree(DLT1_networknoprof_grup)## $res
## [1] 9 0 4 4 7 5 10 6 3 2 24 5 5 3 9 0 7 4 8 0 2 8 6 16 10
## [26] 7 10 0 10 14 1 9 7 9 7 11 8 5 7 0 0 0 0 24 0 2 0 0 6 9
## [51] 7 6 7 16 2 0 8 11 16 23 16 1 8 22 2 5 12 13 7 3 5 3 2 9 4
## [76] 0 1 1 3 4 7 0 1 0 1 0 6 9 1 3 13 11 1 4 6 5 3 5 2 5
## [101] 6 1 8 8 3 6 12 0 3 0 0 4 9 2 8 27 6 6 5 2 5 3 4 1 1
## [126] 1 1 14 10 1 2 1 5 0 0 10 12 4 2 3 3 7 3 9 2 1 3 2 1 2
## [151] 2 4 3 6 1 2 5 7 4 2 4 4 3 1 6 2 11 2 3 5 4 5 8 2 5
## [176] 6 0 6 3 1 0 0 1 2 7 1 2 2 2 1 0 0 17 0 4 0 2 14 4 2
## [201] 12 5 3 0 13 2 3 6 0 0 0 2 1 1 1 4 7 3 21 0 0 0 11 1 0
## [226] 10 0 0 0 1 0 0 1 36 1 1 1 2 2 1 2 2 1 1 2 0 2 0 3 2
## [251] 4 1 1 2 1 4 1 2 1 1 1 1 1 1 3 5 0 8 3 2 0 2 0 0 0
## [276] 1 0 2 0 1 5 0 1 1 6 2 0 1 0 0 0 1 0 1 3 1 0 0 0 1
## [301] 10 2 5 1 1 0 0 0 1 25 1 1 1 1 2 1 3 1 3 1 0 0 1 3 0
## [326] 0 0 1 3 2 2 1 0 2 0 7 0 1 1 2 7 3 1 1 2 1 3 1 1 5
## [351] 4 0 1 0 1 3 0 1 0 3 18 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [426] 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0
##
## $centralization
## [1] 0.03719723
##
## $theoretical_max
## [1] 390728centr_degree(DLT1_Networknoprof_ungrup)## $res
## [1] 36 7 2 8 16 18 35 18 4 15 74 16 28 19 18 3 11 10 56 3 0 5 0 29 0
## [26] 23 16 1 22 58 0 5 14 28 24 28 3 3 6 1 35 19 8 67 2 8 4 5 29 17
## [51] 8 9 27 18 0 17 2 18 2 33 24 26 20 20 0 1 15 30 1 0 0 1 0 6 3
## [76] 4 7 3 1 3 1 5 8 5 4 1 2 14 0 0 3 20 2 0 0 1 1 21 2 20
## [101] 4 1 2 12 1 0 6 4 15 3 1 0 0 7 14 6 0 0 2 0 2 0 1 0 0
## [126] 0 0 8 1 0 1 5 2 1 2 9 14 1 1 0 0 3 0 11 0 0 8 5 0 0
## [151] 0 0 0 0 13 1 1 3 0 0 13 0 6 0 1 1 1 0 0 2 0 0 0 0 0
## [176] 3 12 1 1 0 1 10 9 7 3 0 0 0 0 1 4 17 0 5 5 4 4 17 5 0
## [201] 5 6 7 1 3 0 11 1 2 1 8 5 0 0 2 3 3 1 6 1 9 2 22 1 1
## [226] 1 2 1 1 2 7 1 0 0 0 0 0 0 0 0 0 0 5 0 0 3 3 7 3 0
## [251] 0 1 0 0 0 0 2 0 1 0 0 1 0 0 0 0 1 1 0 0 2 0 4 1 3
## [276] 0 2 1 11 0 1 1 0 0 0 0 4 1 3 1 1 0 2 0 1 0 1 1 1 12
## [301] 0 0 0 0 0 4 3 7 0 2 0 0 0 0 0 0 0 0 0 0 2 4 0 0 1
## [326] 1 1 0 1 0 0 0 1 0 4 3 3 0 0 0 3 0 0 0 0 0 0 0 0 0
## [351] 0 1 1 1 0 0 1 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0
## [426] 0 0 0 0 0 0 19 0 2 0 0 2 1 2 0 0 0 0
##
## $centralization
## [1] 0.07917528
##
## $theoretical_max
## [1] 390728centr_degree(dlt1_network_grup)## $res
## [1] 12 0 4 6 9 8 17 10 4 2 32 5 5 5 11 0 9 6
## [19] 9 0 2 12 6 21 12 7 13 0 11 18 1 10 9 10 8 14
## [37] 8 6 11 0 0 0 0 28 1 2 0 0 8 11 9 6 8 21
## [55] 4 0 9 16 18 27 19 2 12 24 3 7 13 16 8 3 8 4
## [73] 2 9 6 0 1 3 3 4 8 1 6 0 3 0 10 11 1 4
## [91] 14 11 1 7 7 10 4 6 4 8 7 4 10 9 4 6 13 0
## [109] 7 1 0 5 10 2 12 29 6 6 5 3 7 3 4 2 1 3
## [127] 2 16 13 1 4 1 7 1 0 12 14 8 3 3 5 9 5 10
## [145] 2 2 4 2 1 4 2 5 3 10 3 2 8 10 5 3 4 6
## [163] 4 1 7 3 12 2 5 7 7 8 9 2 5 7 0 7 5 1
## [181] 1 0 2 2 8 2 3 4 3 4 0 1 22 1 7 1 3 15
## [199] 4 5 13 6 4 1 13 3 4 6 0 0 1 4 1 2 1 4
## [217] 8 3 23 0 1 0 17 1 0 14 0 0 0 1 0 2 1 37
## [235] 4 3 1 2 3 2 3 4 2 1 3 3 5 1 9 4 5 2
## [253] 3 2 2 5 1 5 2 1 2 1 1 2 4 5 1 8 3 2
## [271] 1 2 0 0 0 5 0 2 1 3 7 0 1 2 6 2 0 1
## [289] 0 0 0 2 0 1 4 1 1 2 1 2 14 5 7 2 2 0
## [307] 3 0 1 27 1 1 1 2 2 1 6 4 4 3 0 1 3 4
## [325] 0 1 0 1 7 3 6 1 1 2 2 8 1 4 2 3 8 4
## [343] 2 2 3 3 5 1 1 6 6 0 1 2 4 5 1 2 0 4
## [361] 18 1 1 1 1 2 1 1 1 1 1 1 1 1 1 3 2 2
## [379] 2 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## [397] 3 1 2 1 1 1 2 1 1 3 1 1 2 3 0 1 2 1
## [415] 2 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 22
## [433] 2 0 0 0 0 0 0 0 0 0 0 416 149
##
## $centralization
## [1] 0.4631625
##
## $theoretical_max
## [1] 394272centr_degree(DLT1_Network_ungrup)## $res
## [1] 41 7 2 10 23 22 39 21 10 18 88 18 30 20 20 3 13 12
## [19] 70 3 0 11 0 33 0 27 18 1 24 72 0 5 14 29 26 33
## [37] 3 3 7 1 35 19 8 85 2 9 5 6 31 19 10 11 27 20
## [55] 0 24 2 18 3 38 32 31 24 21 0 1 17 44 1 1 5 2
## [73] 0 7 4 5 10 3 1 3 1 6 8 5 4 1 2 19 0 0
## [91] 4 22 2 0 0 2 1 22 2 21 5 2 4 12 1 0 6 4
## [109] 15 3 1 0 0 9 14 6 0 0 2 0 4 0 1 0 0 0
## [127] 0 8 1 0 1 5 2 1 2 13 16 1 1 0 0 5 1 11
## [145] 0 1 9 6 0 0 0 0 0 0 15 1 3 5 0 0 14 1
## [163] 8 0 1 2 2 0 0 2 0 0 0 0 0 7 15 1 1 0
## [181] 2 10 10 10 6 0 0 0 0 1 4 17 0 5 5 4 4 21
## [199] 8 0 6 7 8 1 4 0 12 1 3 2 9 8 0 0 4 3
## [217] 5 2 7 1 9 2 22 1 1 1 2 2 1 2 7 1 0 0
## [235] 0 0 0 0 0 0 0 0 6 0 0 3 3 8 3 0 0 1
## [253] 0 0 0 0 3 0 1 0 0 3 0 0 0 0 1 1 0 0
## [271] 3 0 4 1 5 0 2 3 11 0 1 1 0 0 0 0 4 1
## [289] 3 1 1 0 3 0 1 0 1 1 1 13 0 0 0 0 0 4
## [307] 3 8 0 3 0 0 0 0 0 0 0 0 0 0 2 4 0 0
## [325] 2 2 1 0 2 0 0 0 1 0 4 4 3 0 0 0 3 0
## [343] 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0
## [361] 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [379] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## [415] 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 19
## [433] 0 3 1 1 2 2 2 1 0 0 0 165 166
##
## $centralization
## [1] 0.1810425
##
## $theoretical_max
## [1] 394272#cetnralization
centr_degree(DLT1_networknoprof_grup, mode = "out")## $res
## [1] 5 0 2 3 3 5 5 5 3 2 16 3 1 2 3 0 5 4 7 0 0 6 0 12 5
## [26] 4 6 0 6 9 1 4 4 5 4 2 4 0 5 0 0 0 0 15 0 2 0 0 6 4
## [51] 4 5 3 8 2 0 3 5 10 11 3 0 5 9 1 2 4 8 3 0 4 0 2 5 3
## [76] 0 1 1 3 4 4 0 0 0 0 0 4 3 1 2 6 8 1 0 2 5 3 1 0 1
## [101] 0 1 3 8 1 4 8 0 3 0 0 1 6 0 4 2 1 5 1 1 4 1 4 1 1
## [126] 1 1 7 4 1 1 1 4 0 0 5 4 2 2 3 2 5 3 5 1 0 3 2 1 0
## [151] 1 1 0 3 1 1 2 3 3 2 4 1 2 1 3 2 1 2 3 4 3 3 5 1 0
## [176] 3 0 3 3 1 0 0 1 2 6 1 0 1 0 1 0 0 7 0 0 0 1 7 1 1
## [201] 6 5 0 0 8 1 0 1 0 0 0 2 1 1 1 1 3 1 9 0 0 0 5 1 0
## [226] 3 0 0 0 1 0 0 1 0 1 1 1 2 1 1 2 2 1 1 2 0 0 0 0 1
## [251] 1 0 0 1 1 2 1 1 1 1 0 1 0 1 0 4 0 1 3 1 0 1 0 0 0
## [276] 1 0 2 0 1 1 0 1 0 4 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0
## [301] 1 1 0 1 0 0 0 0 1 0 1 1 1 1 2 1 2 0 1 1 0 0 1 0 0
## [326] 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0
## [351] 0 0 1 0 1 0 0 0 0 3 17 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [426] 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0
##
## $centralization
## [1] 0.0439006
##
## $theoretical_max
## [1] 195806centr_degree(DLT1_Networknoprof_ungrup, mode = "out")## $res
## [1] 21 5 2 7 6 9 13 7 3 8 38 12 12 16 11 1 11 10 20 2 0 1 0 16 0
## [26] 5 9 1 15 34 0 0 1 17 13 12 0 2 4 1 6 9 7 34 1 6 4 4 13 7
## [51] 5 6 12 13 0 15 1 9 2 21 10 11 12 8 0 1 2 16 1 0 0 1 0 4 2
## [76] 3 4 0 1 2 1 4 8 0 4 1 2 4 0 0 3 7 1 0 0 1 1 9 2 3
## [101] 1 1 2 6 1 0 6 4 12 1 1 0 0 3 0 0 0 0 1 0 2 0 1 0 0
## [126] 0 0 5 1 0 1 1 1 1 2 3 2 1 1 0 0 2 0 6 0 0 7 5 0 0
## [151] 0 0 0 0 5 1 1 1 0 0 7 0 5 0 1 1 1 0 0 1 0 0 0 0 0
## [176] 0 3 1 1 0 0 0 6 4 3 0 0 0 0 1 3 1 0 0 2 4 4 7 3 0
## [201] 2 6 1 1 3 0 6 1 0 1 2 1 0 0 2 1 1 1 1 1 1 1 5 1 1
## [226] 1 1 1 1 2 7 1 0 0 0 0 0 0 0 0 0 0 1 0 0 2 3 2 2 0
## [251] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 2
## [276] 0 2 1 3 0 1 1 0 0 0 0 1 1 3 1 1 0 0 0 0 0 1 1 1 2
## [301] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## [326] 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## [351] 0 1 1 1 0 0 1 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## [426] 0 0 0 0 0 0 16 0 1 0 0 1 0 1 0 0 0 0
##
## $centralization
## [1] 0.081259
##
## $theoretical_max
## [1] 195806centr_degree(dlt1_network_grup, mode = "out")## $res
## [1] 7 0 2 5 4 8 10 8 4 2 22 3 1 3 5 0 7 6 8 0 0 9 0 15 6
## [26] 4 8 0 6 13 1 4 5 5 5 5 4 0 8 0 0 0 0 18 1 2 0 0 7 6
## [51] 6 5 4 12 4 0 3 7 11 15 5 1 9 10 2 3 5 10 4 0 7 1 2 5 4
## [76] 0 1 3 3 4 5 1 5 0 2 0 7 5 1 3 7 8 1 1 3 10 4 2 1 4
## [101] 1 4 5 9 2 4 9 0 6 1 0 2 6 0 6 3 1 5 1 2 6 1 4 2 1
## [126] 3 2 9 7 1 3 1 6 1 0 6 5 5 3 3 3 6 5 6 1 1 4 2 1 2
## [151] 1 2 0 6 3 1 5 6 4 3 4 2 3 1 4 3 2 2 5 6 6 6 6 1 0
## [176] 4 0 4 5 1 1 0 2 2 7 2 0 3 0 3 0 1 11 1 2 1 2 8 1 3
## [201] 7 6 1 1 8 2 0 1 0 0 1 4 1 2 1 1 4 1 11 0 1 0 10 1 0
## [226] 7 0 0 0 1 0 2 1 0 3 3 1 2 2 2 3 4 2 1 3 3 3 1 5 3
## [251] 1 1 1 1 2 2 1 4 2 1 1 1 0 2 1 4 1 1 3 1 1 1 0 0 0
## [276] 4 0 2 1 3 2 0 1 1 4 0 0 1 0 0 0 1 0 1 1 1 1 2 1 1
## [301] 3 4 2 2 1 0 2 0 1 0 1 1 1 2 2 1 5 2 2 3 0 1 2 0 0
## [326] 1 0 1 5 2 4 1 1 0 2 1 1 3 1 2 1 1 1 1 1 2 3 1 1 1
## [351] 1 0 1 2 3 2 1 1 0 4 17 1 1 1 1 2 1 1 1 1 1 1 1 1 1
## [376] 3 2 2 2 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 3 1 2 1
## [401] 1 1 2 1 1 3 1 1 2 3 0 1 1 1 2 1 1 1 1 1 1 0 1 1 1
## [426] 1 1 1 1 1 1 21 1 0 0 0 0 0 0 0 0 0 0 55 40
##
## $centralization
## [1] 0.1175271
##
## $theoretical_max
## [1] 197580centr_degree(DLT1_Network_ungrup, mode = "out")## $res
## [1] 26 5 2 9 12 13 15 10 8 10 51 14 14 17 13 1 13 12 27 2 0 6 0 19 0
## [26] 8 10 1 17 45 0 0 1 17 14 16 0 2 5 1 6 9 7 49 1 7 5 4 15 8
## [51] 7 8 12 15 0 20 1 9 3 25 17 16 16 9 0 1 4 29 1 1 5 2 0 5 3
## [76] 4 7 0 1 2 1 5 8 0 4 1 2 9 0 0 4 8 1 0 0 2 1 10 2 3
## [101] 2 2 4 6 1 0 6 4 12 1 1 0 0 5 0 0 0 0 1 0 4 0 1 0 0
## [126] 0 0 5 1 0 1 1 1 1 2 7 3 1 1 0 0 3 1 6 0 1 8 6 0 0
## [151] 0 0 0 0 6 1 3 1 0 0 8 1 7 0 1 2 1 0 0 1 0 0 0 0 0
## [176] 4 6 1 1 0 0 0 7 7 6 0 0 0 0 1 3 1 0 0 2 4 4 8 5 0
## [201] 3 7 1 1 4 0 7 1 1 2 3 3 0 0 4 1 2 1 2 1 1 1 5 1 1
## [226] 1 1 2 1 2 7 1 0 0 0 0 0 0 0 0 0 0 1 0 0 2 3 2 2 0
## [251] 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 4
## [276] 0 2 3 3 0 1 1 0 0 0 0 1 1 3 1 1 0 1 0 0 0 1 1 1 2
## [301] 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## [326] 2 1 0 1 0 0 0 1 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## [351] 0 1 1 1 0 0 1 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## [426] 0 0 0 0 0 0 16 0 2 1 1 1 1 1 1 0 0 0 51 15
##
## $centralization
## [1] 0.1085636
##
## $theoretical_max
## [1] 197580centr_degree(DLT1_networknoprof_grup, mode = "in")## $res
## [1] 4 0 2 1 4 0 5 1 0 0 8 2 4 1 6 0 2 0 1 0 2 2 6 4 5
## [26] 3 4 0 4 5 0 5 3 4 3 9 4 5 2 0 0 0 0 9 0 0 0 0 0 5
## [51] 3 1 4 8 0 0 5 6 6 12 13 1 3 13 1 3 8 5 4 3 1 3 0 4 1
## [76] 0 0 0 0 0 3 0 1 0 1 0 2 6 0 1 7 3 0 4 4 0 0 4 2 4
## [101] 6 0 5 0 2 2 4 0 0 0 0 3 3 2 4 25 5 1 4 1 1 2 0 0 0
## [126] 0 0 7 6 0 1 0 1 0 0 5 8 2 0 0 1 2 0 4 1 1 0 0 0 2
## [151] 1 3 3 3 0 1 3 4 1 0 0 3 1 0 3 0 10 0 0 1 1 2 3 1 5
## [176] 3 0 3 0 0 0 0 0 0 1 0 2 1 2 0 0 0 10 0 4 0 1 7 3 1
## [201] 6 0 3 0 5 1 3 5 0 0 0 0 0 0 0 3 4 2 12 0 0 0 6 0 0
## [226] 7 0 0 0 0 0 0 0 36 0 0 0 0 1 0 0 0 0 0 0 0 2 0 3 1
## [251] 3 1 1 1 0 2 0 1 0 0 1 0 1 0 3 1 0 7 0 1 0 1 0 0 0
## [276] 0 0 0 0 0 4 0 0 1 2 2 0 0 0 0 0 1 0 0 3 0 0 0 0 1
## [301] 9 1 5 0 1 0 0 0 0 25 0 0 0 0 0 0 1 1 2 0 0 0 0 3 0
## [326] 0 0 0 2 1 1 0 0 2 0 7 0 1 1 1 7 3 1 1 2 1 2 0 0 5
## [351] 4 0 0 0 0 3 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [426] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
##
## $centralization
## [1] 0.07783725
##
## $theoretical_max
## [1] 195806centr_degree(DLT1_Networknoprof_ungrup, mode = "in")## $res
## [1] 15 2 0 1 10 9 22 11 1 7 36 4 16 3 7 2 0 0 36 1 0 4 0 13 0
## [26] 18 7 0 7 24 0 5 13 11 11 16 3 1 2 0 29 10 1 33 1 2 0 1 16 10
## [51] 3 3 15 5 0 2 1 9 0 12 14 15 8 12 0 0 13 14 0 0 0 0 0 2 1
## [76] 1 3 3 0 1 0 1 0 5 0 0 0 10 0 0 0 13 1 0 0 0 0 12 0 17
## [101] 3 0 0 6 0 0 0 0 3 2 0 0 0 4 14 6 0 0 1 0 0 0 0 0 0
## [126] 0 0 3 0 0 0 4 1 0 0 6 12 0 0 0 0 1 0 5 0 0 1 0 0 0
## [151] 0 0 0 0 8 0 0 2 0 0 6 0 1 0 0 0 0 0 0 1 0 0 0 0 0
## [176] 3 9 0 0 0 1 10 3 3 0 0 0 0 0 0 1 16 0 5 3 0 0 10 2 0
## [201] 3 0 6 0 0 0 5 0 2 0 6 4 0 0 0 2 2 0 5 0 8 1 17 0 0
## [226] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 1 0 5 1 0
## [251] 0 1 0 0 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 4 0 1
## [276] 0 0 0 8 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 1 0 0 0 0 10
## [301] 0 0 0 0 0 4 2 7 0 2 0 0 0 0 0 0 0 0 0 0 1 4 0 0 0
## [326] 0 0 0 1 0 0 0 0 0 4 2 3 0 0 0 2 0 0 0 0 0 0 0 0 0
## [351] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [376] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
## [426] 0 0 0 0 0 0 3 0 1 0 0 1 1 1 0 0 0 0
##
## $centralization
## [1] 0.07673411
##
## $theoretical_max
## [1] 195806centr_degree(dlt1_network_grup, mode = "in")## $res
## [1] 5 0 2 1 5 0 7 2 0 0 10 2 4 2 6 0 2 0
## [19] 1 0 2 3 6 6 6 3 5 0 5 5 0 6 4 5 3 9
## [37] 4 6 3 0 0 0 0 10 0 0 0 0 1 5 3 1 4 9
## [55] 0 0 6 9 7 12 14 1 3 14 1 4 8 6 4 3 1 3
## [73] 0 4 2 0 0 0 0 0 3 0 1 0 1 0 3 6 0 1
## [91] 7 3 0 6 4 0 0 4 3 4 6 0 5 0 2 2 4 0
## [109] 1 0 0 3 4 2 6 26 5 1 4 1 1 2 0 0 0 0
## [127] 0 7 6 0 1 0 1 0 0 6 9 3 0 0 2 3 0 4
## [145] 1 1 0 0 0 2 1 3 3 4 0 1 3 4 1 0 0 4
## [163] 1 0 3 0 10 0 0 1 1 2 3 1 5 3 0 3 0 0
## [181] 0 0 0 0 1 0 3 1 3 1 0 0 11 0 5 0 1 7
## [199] 3 2 6 0 3 0 5 1 4 5 0 0 0 0 0 0 0 3
## [217] 4 2 12 0 0 0 7 0 0 7 0 0 0 0 0 0 0 37
## [235] 1 0 0 0 1 0 0 0 0 0 0 0 2 0 4 1 4 1
## [253] 2 1 0 3 0 1 0 0 1 0 1 0 3 1 0 7 0 1
## [271] 0 1 0 0 0 1 0 0 0 0 5 0 0 1 2 2 0 0
## [289] 0 0 0 1 0 0 3 0 0 0 0 1 11 1 5 0 1 0
## [307] 1 0 0 27 0 0 0 0 0 0 1 2 2 0 0 0 1 4
## [325] 0 0 0 0 2 1 2 0 0 2 0 7 0 1 1 1 7 3
## [343] 1 1 2 1 2 0 0 5 5 0 0 0 1 3 0 1 0 0
## [361] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [379] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [415] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [433] 1 0 0 0 0 0 0 0 0 0 0 361 109
##
## $centralization
## [1] 0.8067163
##
## $theoretical_max
## [1] 197580centr_degree(DLT1_Network_ungrup, mode = "in")## $res
## [1] 15 2 0 1 11 9 24 11 2 8 37 4 16 3 7 2 0 0
## [19] 43 1 0 5 0 14 0 19 8 0 7 27 0 5 13 12 12 17
## [37] 3 1 2 0 29 10 1 36 1 2 0 2 16 11 3 3 15 5
## [55] 0 4 1 9 0 13 15 15 8 12 0 0 13 15 0 0 0 0
## [73] 0 2 1 1 3 3 0 1 0 1 0 5 0 0 0 10 0 0
## [91] 0 14 1 0 0 0 0 12 0 18 3 0 0 6 0 0 0 0
## [109] 3 2 0 0 0 4 14 6 0 0 1 0 0 0 0 0 0 0
## [127] 0 3 0 0 0 4 1 0 0 6 13 0 0 0 0 2 0 5
## [145] 0 0 1 0 0 0 0 0 0 0 9 0 0 4 0 0 6 0
## [163] 1 0 0 0 1 0 0 1 0 0 0 0 0 3 9 0 0 0
## [181] 2 10 3 3 0 0 0 0 0 0 1 16 0 5 3 0 0 13
## [199] 3 0 3 0 7 0 0 0 5 0 2 0 6 5 0 0 0 2
## [217] 3 1 5 0 8 1 17 0 0 0 1 0 0 0 0 0 0 0
## [235] 0 0 0 0 0 0 0 0 5 0 0 1 0 6 1 0 0 1
## [253] 0 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0
## [271] 2 0 4 0 1 0 0 0 8 0 0 0 0 0 0 0 3 0
## [289] 0 0 0 0 2 0 1 0 0 0 0 11 0 0 0 0 0 4
## [307] 2 8 0 2 0 0 0 0 0 0 0 0 0 0 1 4 0 0
## [325] 1 0 0 0 1 0 0 0 0 0 4 2 3 0 0 0 2 0
## [343] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [379] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [415] 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 3
## [433] 0 1 0 0 1 1 1 0 0 0 0 114 151
##
## $centralization
## [1] 0.3337888
##
## $theoretical_max
## [1] 197580centr_betw(DLT1_networknoprof_grup)## $res
## [1] 241.557937 0.000000 662.940476 1003.845915 473.637302 0.000000
## [7] 3914.583594 4.000000 0.000000 0.000000 291.893318 239.421351
## [13] 4.000000 42.615709 388.216667 0.000000 801.517856 0.000000
## [19] 1793.072854 0.000000 0.000000 0.000000 0.000000 890.023810
## [25] 1122.950192 598.264918 229.033333 0.000000 136.333333 753.975504
## [31] 0.000000 69.000000 174.633333 186.222222 349.859936 253.712698
## [37] 28.000000 0.000000 131.166667 0.000000 0.000000 0.000000
## [43] 0.000000 2134.454851 0.000000 0.000000 0.000000 0.000000
## [49] 0.000000 52.666667 86.083333 50.026984 591.547158 742.584127
## [55] 0.000000 0.000000 142.785714 111.119697 4452.906830 1743.848871
## [61] 1894.213203 0.000000 85.383333 1518.026899 0.000000 149.033333
## [67] 3957.911905 428.874847 305.788636 0.000000 97.382051 0.000000
## [73] 0.000000 1653.862393 420.461905 0.000000 0.000000 0.000000
## [79] 0.000000 0.000000 355.132051 0.000000 0.000000 0.000000
## [85] 0.000000 0.000000 387.662114 678.486308 0.000000 45.380059
## [91] 2490.684490 610.318029 0.000000 0.000000 5.000000 0.000000
## [97] 0.000000 368.924370 0.000000 5.033333 0.000000 0.000000
## [103] 259.666667 0.000000 10.333333 207.684991 759.677545 0.000000
## [109] 0.000000 0.000000 0.000000 4.000000 54.133333 0.000000
## [115] 130.300000 0.000000 9.333333 275.500000 20.600000 1.000000
## [121] 102.031746 1.500000 0.000000 0.000000 0.000000 0.000000
## [127] 0.000000 6800.291498 608.074373 0.000000 1.000000 0.000000
## [133] 70.459524 0.000000 0.000000 289.166667 3141.603391 149.000000
## [139] 0.000000 0.000000 1.000000 32.000000 0.000000 102.150000
## [145] 143.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [151] 1.000000 79.944444 0.000000 258.555556 0.000000 72.500000
## [157] 29.666667 185.202076 87.166667 0.000000 0.000000 3541.959524
## [163] 4.000000 0.000000 1529.623810 0.000000 0.000000 0.000000
## [169] 0.000000 1.000000 2.000000 193.042857 57.666667 2.500000
## [175] 0.000000 172.500000 0.000000 110.000000 0.000000 0.000000
## [181] 0.000000 0.000000 0.000000 0.000000 369.275397 0.000000
## [187] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [193] 679.912301 0.000000 0.000000 0.000000 1.000000 1017.812210
## [199] 1067.309524 21.916667 312.500000 0.000000 0.000000 0.000000
## [205] 2329.811511 1.250000 0.000000 27.766667 0.000000 0.000000
## [211] 0.000000 0.000000 0.000000 0.000000 0.000000 427.000000
## [217] 2.000000 73.000000 5548.973343 0.000000 0.000000 0.000000
## [223] 136.497619 0.000000 0.000000 3868.819361 0.000000 0.000000
## [229] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [235] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [241] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [247] 0.000000 0.000000 0.000000 85.083333 0.000000 0.000000
## [253] 0.000000 0.500000 0.000000 160.394444 0.000000 23.513492
## [259] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [265] 0.000000 277.900000 0.000000 1409.823791 0.000000 0.000000
## [271] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [277] 0.000000 0.000000 0.000000 0.000000 2902.823296 0.000000
## [283] 0.000000 0.000000 186.850000 0.000000 0.000000 0.000000
## [289] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [295] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [301] 130.500000 13.541858 0.000000 0.000000 0.000000 0.000000
## [307] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [313] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [319] 101.333333 0.000000 0.000000 0.000000 0.000000 0.000000
## [325] 0.000000 0.000000 0.000000 0.000000 2043.416667 1.000000
## [331] 3.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [337] 0.000000 0.000000 0.000000 111.000000 0.000000 0.000000
## [343] 0.000000 0.000000 0.000000 0.000000 323.448918 0.000000
## [349] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [355] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [361] 163.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [367] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [373] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [379] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [385] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [391] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [397] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [403] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [409] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [415] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [421] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [427] 0.000000 0.000000 0.000000 0.000000 0.000000 5712.129181
## [433] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [439] 0.000000 0.000000 0.000000 0.000000 0.000000
##
## $centralization
## [1] 0.03392868
##
## $theoretical_max
## [1] 86155524centr_betw(DLT1_Networknoprof_ungrup)## $res
## [1] 2155.4385787 65.2463351 0.0000000 17.4926511 228.1931749
## [6] 355.4120654 1295.0712933 191.4416023 5.9335124 248.4366422
## [11] 4111.8080056 148.7266436 654.5271335 249.9244358 566.2110113
## [16] 5.0099567 0.0000000 0.0000000 5308.6959591 74.8936869
## [21] 0.0000000 57.4364002 0.0000000 1204.4327025 0.0000000
## [26] 138.5146452 473.6203038 0.0000000 1050.5896228 3822.9137964
## [31] 0.0000000 0.0000000 0.0000000 378.5101967 1218.1796605
## [36] 2409.7165167 0.0000000 0.3357466 78.0426891 0.0000000
## [41] 824.0980041 942.9008540 40.8871960 5665.5818466 1.6888889
## [46] 28.7024133 0.0000000 181.2916667 757.6212050 512.2702946
## [51] 22.0735205 33.9695104 1019.1295087 1054.4210350 0.0000000
## [56] 189.3721538 183.0000000 359.1522506 0.0000000 2056.2647710
## [61] 722.5570645 428.7786049 321.4998418 843.8817916 0.0000000
## [66] 0.0000000 302.3373660 652.6970360 0.0000000 0.0000000
## [71] 0.0000000 0.0000000 0.0000000 41.6855076 6.0177524
## [76] 21.7145849 209.4137873 0.0000000 0.0000000 12.2791632
## [81] 0.0000000 132.0000000 0.0000000 0.0000000 0.0000000
## [86] 0.0000000 0.0000000 219.7165127 0.0000000 0.0000000
## [91] 0.0000000 230.2968851 1.0000000 0.0000000 0.0000000
## [96] 0.0000000 0.0000000 401.9190416 0.0000000 786.7977556
## [101] 128.1000000 0.0000000 0.0000000 1028.3664041 0.0000000
## [106] 0.0000000 0.0000000 0.0000000 794.0677495 133.0000000
## [111] 0.0000000 0.0000000 0.0000000 36.1346763 0.0000000
## [116] 0.0000000 0.0000000 0.0000000 19.3164502 0.0000000
## [121] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [126] 0.0000000 0.0000000 150.5983297 0.0000000 0.0000000
## [131] 0.0000000 255.2530021 1.5434783 0.0000000 0.0000000
## [136] 72.1857582 97.2940564 0.0000000 0.0000000 0.0000000
## [141] 0.0000000 0.0000000 0.0000000 63.6854917 0.0000000
## [146] 0.0000000 32.4098052 0.0000000 0.0000000 0.0000000
## [151] 0.0000000 0.0000000 0.0000000 0.0000000 432.9507235
## [156] 0.0000000 0.0000000 3.6121212 0.0000000 0.0000000
## [161] 703.0378095 0.0000000 183.7045518 0.0000000 0.0000000
## [166] 0.0000000 0.0000000 0.0000000 0.0000000 9.5222222
## [171] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [176] 0.0000000 397.8237501 0.0000000 0.0000000 0.0000000
## [181] 0.0000000 0.0000000 105.4288043 33.6798190 0.0000000
## [186] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [191] 116.2551850 738.4490748 0.0000000 0.0000000 31.4335068
## [196] 0.0000000 0.0000000 123.1589627 43.7424883 0.0000000
## [201] 25.0948814 0.0000000 161.0058161 0.0000000 0.0000000
## [206] 0.0000000 169.9237198 0.0000000 0.0000000 0.0000000
## [211] 324.2960286 37.2242535 0.0000000 0.0000000 0.0000000
## [216] 2.2738095 250.0000000 0.0000000 7.4570195 0.0000000
## [221] 674.7717475 0.0000000 823.2991645 0.0000000 0.0000000
## [226] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [231] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [236] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [241] 0.0000000 0.0000000 382.4661104 0.0000000 0.0000000
## [246] 25.2862745 0.0000000 157.9135195 1.9761905 0.0000000
## [251] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [256] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [261] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [266] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [271] 0.0000000 0.0000000 0.0000000 0.0000000 79.9632353
## [276] 0.0000000 0.0000000 0.0000000 758.0534266 0.0000000
## [281] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [286] 0.0000000 4.5259710 0.0000000 0.0000000 0.0000000
## [291] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [296] 0.0000000 0.0000000 0.0000000 0.0000000 428.3884241
## [301] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [306] 0.0000000 2.0000000 0.0000000 0.0000000 0.0000000
## [311] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [316] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [321] 1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [326] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [331] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [336] 1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [341] 14.0314574 0.0000000 0.0000000 0.0000000 0.0000000
## [346] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [351] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [356] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [361] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [366] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [371] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [376] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [381] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [386] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [391] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [396] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [401] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [406] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [411] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [416] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [421] 0.0000000 134.9499856 0.0000000 0.0000000 0.0000000
## [426] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [431] 0.0000000 473.5719609 0.0000000 0.0000000 0.0000000
## [436] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [441] 0.0000000 0.0000000 0.0000000
##
## $centralization
## [1] 0.02847706
##
## $theoretical_max
## [1] 86155524centr_betw(dlt1_network_grup)## $res
## [1] 2.620334e+03 0.000000e+00 3.878709e+00 2.526948e+01 6.641883e+02
## [6] 0.000000e+00 1.037055e+03 3.868731e+02 0.000000e+00 0.000000e+00
## [11] 2.929510e+03 8.791941e+00 4.392143e+01 5.571314e+01 9.882039e+02
## [16] 0.000000e+00 1.788664e+02 0.000000e+00 1.107691e+03 0.000000e+00
## [21] 0.000000e+00 1.393933e+02 0.000000e+00 5.959195e+02 2.935622e+02
## [26] 3.711200e+02 7.769728e+02 0.000000e+00 8.398376e+02 8.163246e+02
## [31] 0.000000e+00 4.587656e+02 1.165318e+03 6.854304e+02 1.297627e+01
## [36] 8.181974e+02 1.871736e+02 0.000000e+00 3.520789e+02 0.000000e+00
## [41] 0.000000e+00 0.000000e+00 0.000000e+00 3.963140e+03 0.000000e+00
## [46] 0.000000e+00 0.000000e+00 0.000000e+00 2.220143e+03 4.258915e+01
## [51] 2.555771e+01 7.105195e+00 5.417222e+02 1.788216e+03 0.000000e+00
## [56] 0.000000e+00 5.144108e+01 8.509436e+02 3.097242e+03 1.264761e+03
## [61] 1.083544e+03 0.000000e+00 1.166453e+02 3.325611e+03 0.000000e+00
## [66] 9.764598e+01 3.327964e+02 1.102473e+03 2.187149e+02 0.000000e+00
## [71] 2.550741e+01 1.621020e+02 0.000000e+00 1.157969e+03 2.438139e+03
## [76] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [81] 4.327334e+02 0.000000e+00 0.000000e+00 0.000000e+00 6.496612e+00
## [86] 0.000000e+00 6.870028e+02 7.752808e+02 0.000000e+00 3.500000e+00
## [91] 1.775496e+03 2.616953e+03 0.000000e+00 1.830000e+02 1.875000e+02
## [96] 0.000000e+00 0.000000e+00 1.042994e+02 6.982722e+02 1.007194e+02
## [101] 1.627788e+02 0.000000e+00 1.952019e+02 0.000000e+00 1.233058e+01
## [106] 3.080914e+02 7.799748e+02 0.000000e+00 2.299616e+03 0.000000e+00
## [111] 0.000000e+00 1.332912e+02 7.028700e+02 0.000000e+00 7.605845e+02
## [116] 1.024280e+03 5.312277e+00 7.174000e+02 5.258766e+00 1.840000e+02
## [121] 2.978426e+02 1.500000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [126] 0.000000e+00 0.000000e+00 3.731028e+03 1.430175e+03 0.000000e+00
## [131] 3.123260e+01 0.000000e+00 2.418522e+01 0.000000e+00 0.000000e+00
## [136] 2.684000e+02 2.503229e+03 3.880298e+02 0.000000e+00 0.000000e+00
## [141] 1.337696e+02 5.360028e+02 0.000000e+00 2.397665e+02 2.846748e+01
## [146] 7.111905e+01 0.000000e+00 0.000000e+00 0.000000e+00 3.680000e+02
## [151] 1.000000e+00 1.782251e+01 0.000000e+00 1.338736e+03 0.000000e+00
## [156] 1.654167e+01 1.534789e+01 1.302612e+02 1.086892e+03 0.000000e+00
## [161] 0.000000e+00 1.012441e+03 8.035714e-01 0.000000e+00 3.659440e+02
## [166] 0.000000e+00 1.820000e+02 0.000000e+00 0.000000e+00 1.000000e+00
## [171] 1.840000e+02 3.607261e+00 9.500884e+02 1.560456e+01 0.000000e+00
## [176] 3.788416e+02 0.000000e+00 3.671793e+02 0.000000e+00 0.000000e+00
## [181] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 6.880897e+01
## [186] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.036227e+00
## [191] 0.000000e+00 0.000000e+00 1.630303e+03 0.000000e+00 3.616825e+01
## [196] 0.000000e+00 1.000000e+00 1.643140e+03 5.287722e+00 3.501650e+02
## [201] 6.702007e+02 0.000000e+00 0.000000e+00 0.000000e+00 4.840840e+02
## [206] 9.429252e+00 0.000000e+00 7.156881e+00 0.000000e+00 0.000000e+00
## [211] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [216] 7.440528e+00 1.896040e+02 9.769444e+00 4.958851e+03 0.000000e+00
## [221] 0.000000e+00 0.000000e+00 8.825825e+02 0.000000e+00 0.000000e+00
## [226] 6.568763e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [231] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.014004e+00
## [236] 0.000000e+00 0.000000e+00 0.000000e+00 9.050000e+01 0.000000e+00
## [241] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [246] 0.000000e+00 2.061837e+02 0.000000e+00 7.174896e+02 6.834957e+00
## [251] 1.159524e+02 0.000000e+00 0.000000e+00 2.129799e+00 0.000000e+00
## [256] 2.180269e+03 0.000000e+00 1.964850e+01 0.000000e+00 0.000000e+00
## [261] 1.840000e+02 0.000000e+00 0.000000e+00 0.000000e+00 2.416240e+01
## [266] 6.341087e+02 0.000000e+00 2.446499e+02 0.000000e+00 0.000000e+00
## [271] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [276] 2.160768e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [281] 2.494601e+03 0.000000e+00 0.000000e+00 1.840000e+02 1.833173e+01
## [286] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [291] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.830000e+02
## [296] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [301] 3.018211e+02 2.995238e+00 7.707078e+01 0.000000e+00 0.000000e+00
## [306] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [311] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [316] 0.000000e+00 4.198429e+01 0.000000e+00 2.152383e+02 0.000000e+00
## [321] 0.000000e+00 0.000000e+00 3.590000e+02 0.000000e+00 0.000000e+00
## [326] 0.000000e+00 0.000000e+00 0.000000e+00 2.990965e+02 7.776626e+01
## [331] 4.390411e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [336] 1.217797e+02 0.000000e+00 0.000000e+00 0.000000e+00 3.767570e+02
## [341] 7.625302e+01 1.830000e+02 0.000000e+00 1.840000e+02 0.000000e+00
## [346] 4.832479e+01 2.139286e+02 0.000000e+00 0.000000e+00 4.380041e+01
## [351] 6.279229e+01 0.000000e+00 0.000000e+00 0.000000e+00 1.378355e+02
## [356] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [361] 1.840000e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [366] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [371] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [376] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [381] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [386] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [391] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [396] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [401] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [406] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [411] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [416] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [421] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [426] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [431] 0.000000e+00 2.167043e+03 0.000000e+00 0.000000e+00 0.000000e+00
## [436] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [441] 0.000000e+00 0.000000e+00 0.000000e+00 5.179252e+04 1.581162e+04
##
## $centralization
## [1] 0.2620236
##
## $theoretical_max
## [1] 87331248centr_betw(DLT1_Network_ungrup)## $res
## [1] 1.836674e+03 5.295304e+01 0.000000e+00 8.745172e+00 3.006301e+02
## [6] 3.478272e+02 1.273745e+03 9.987246e+01 1.935938e+00 2.814417e+02
## [11] 2.788080e+03 5.643970e+01 3.719028e+02 2.413616e+02 2.349229e+02
## [16] 2.717384e+00 0.000000e+00 0.000000e+00 4.701886e+03 6.859632e+01
## [21] 0.000000e+00 3.662573e+01 0.000000e+00 1.014463e+03 0.000000e+00
## [26] 1.174601e+02 5.748576e+02 0.000000e+00 9.125814e+02 3.580352e+03
## [31] 0.000000e+00 0.000000e+00 0.000000e+00 4.113633e+02 1.366315e+03
## [36] 2.034641e+03 0.000000e+00 2.577598e-01 6.592337e+01 0.000000e+00
## [41] 6.813645e+02 3.732940e+02 3.969821e+01 4.707997e+03 1.004382e+00
## [46] 2.070005e+01 0.000000e+00 2.312432e+02 5.849746e+02 5.209948e+02
## [51] 6.994953e+00 1.831430e+01 7.542679e+02 3.501220e+02 0.000000e+00
## [56] 8.025524e+02 2.060000e+02 2.290121e+02 0.000000e+00 1.465714e+03
## [61] 7.428969e+02 2.285033e+02 1.943786e+02 8.275335e+02 0.000000e+00
## [66] 0.000000e+00 3.832640e+02 4.232772e+02 0.000000e+00 0.000000e+00
## [71] 0.000000e+00 0.000000e+00 0.000000e+00 1.800160e+01 1.645644e+01
## [76] 9.177446e+00 2.265470e+02 0.000000e+00 0.000000e+00 1.131402e+01
## [81] 0.000000e+00 1.410000e+02 0.000000e+00 0.000000e+00 0.000000e+00
## [86] 0.000000e+00 0.000000e+00 8.512826e+01 0.000000e+00 0.000000e+00
## [91] 0.000000e+00 3.342119e+02 1.000000e+00 0.000000e+00 0.000000e+00
## [96] 0.000000e+00 0.000000e+00 2.535533e+02 0.000000e+00 4.350041e+02
## [101] 1.399283e+02 0.000000e+00 0.000000e+00 8.953589e+02 0.000000e+00
## [106] 0.000000e+00 0.000000e+00 0.000000e+00 8.607135e+02 1.430000e+02
## [111] 0.000000e+00 0.000000e+00 0.000000e+00 2.832261e+01 0.000000e+00
## [116] 0.000000e+00 0.000000e+00 0.000000e+00 2.199077e+01 0.000000e+00
## [121] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [126] 0.000000e+00 0.000000e+00 1.128867e+02 0.000000e+00 0.000000e+00
## [131] 0.000000e+00 2.750929e+02 1.358974e+00 0.000000e+00 0.000000e+00
## [136] 4.214415e+01 7.585438e+01 0.000000e+00 0.000000e+00 0.000000e+00
## [141] 0.000000e+00 0.000000e+00 0.000000e+00 3.797100e+01 0.000000e+00
## [146] 0.000000e+00 1.639308e+01 0.000000e+00 0.000000e+00 0.000000e+00
## [151] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.668287e+02
## [156] 0.000000e+00 0.000000e+00 2.428571e+00 0.000000e+00 0.000000e+00
## [161] 2.527588e+02 0.000000e+00 2.104111e+02 0.000000e+00 0.000000e+00
## [166] 0.000000e+00 4.813771e+01 0.000000e+00 0.000000e+00 2.803580e+00
## [171] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [176] 1.525494e+00 2.600201e+02 0.000000e+00 0.000000e+00 0.000000e+00
## [181] 0.000000e+00 0.000000e+00 9.247417e+01 9.642663e+00 0.000000e+00
## [186] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [191] 4.771027e+01 7.410627e+02 0.000000e+00 0.000000e+00 1.106269e+01
## [196] 0.000000e+00 0.000000e+00 3.554489e+02 1.245336e+02 0.000000e+00
## [201] 1.065805e+01 0.000000e+00 1.496131e+02 0.000000e+00 0.000000e+00
## [206] 0.000000e+00 1.485520e+02 0.000000e+00 4.552109e+00 0.000000e+00
## [211] 1.404356e+02 5.792973e+01 0.000000e+00 0.000000e+00 0.000000e+00
## [216] 1.847629e+00 3.361142e+02 0.000000e+00 1.428407e+01 0.000000e+00
## [221] 5.056472e+02 0.000000e+00 7.706795e+02 0.000000e+00 0.000000e+00
## [226] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [231] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [236] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [241] 0.000000e+00 0.000000e+00 4.204025e+02 0.000000e+00 0.000000e+00
## [246] 1.900078e+01 0.000000e+00 1.631086e+02 2.662638e+00 0.000000e+00
## [251] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [256] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [261] 0.000000e+00 3.240260e-01 0.000000e+00 0.000000e+00 0.000000e+00
## [266] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [271] 2.730000e+02 0.000000e+00 0.000000e+00 0.000000e+00 8.380403e+01
## [276] 0.000000e+00 0.000000e+00 0.000000e+00 7.726328e+02 0.000000e+00
## [281] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [286] 0.000000e+00 4.065774e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [291] 0.000000e+00 0.000000e+00 4.955786e+00 0.000000e+00 0.000000e+00
## [296] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.190352e+02
## [301] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [306] 0.000000e+00 2.000000e+00 0.000000e+00 0.000000e+00 1.398232e+01
## [311] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [316] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [321] 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.830222e+00
## [326] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [331] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [336] 7.885846e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [341] 1.027334e+01 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [346] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [351] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [356] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [361] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [366] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [371] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [376] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [381] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [386] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [391] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [396] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [401] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [406] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [411] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [416] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [421] 0.000000e+00 1.415822e+02 0.000000e+00 0.000000e+00 0.000000e+00
## [426] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [431] 0.000000e+00 4.294483e+02 0.000000e+00 1.823864e+00 0.000000e+00
## [436] 0.000000e+00 0.000000e+00 3.240260e-01 0.000000e+00 0.000000e+00
## [441] 0.000000e+00 0.000000e+00 0.000000e+00 1.000871e+04 4.388902e+03
##
## $centralization
## [1] 0.05028412
##
## $theoretical_max
## [1] 87331248centr_clo(DLT1_networknoprof_grup)## $res
## [1] 0.3972603 NaN 0.1678404 0.1363203 0.1486486 0.1342032 0.1997207
## [8] 0.3835616 0.1369216 0.1457490 0.1977870 0.1994421 0.2444444 0.1154157
## [15] 0.2900000 NaN 0.1478800 0.1376060 0.2210201 NaN NaN
## [22] 0.1562842 NaN 0.1838046 0.1812421 0.1475748 0.8571429 NaN
## [29] 0.4230769 0.1651270 0.3066667 0.3142857 0.5116279 0.1442987 0.1421471
## [36] 1.0000000 0.3142857 NaN 0.1537634 NaN NaN NaN
## [43] NaN 0.1765432 NaN 0.1230769 NaN NaN 0.1949934
## [50] 0.3333333 0.3118280 0.1438561 0.1817027 0.4202899 0.1349578 NaN
## [57] 0.1557734 0.1425723 0.2045780 0.1658933 0.1391051 NaN 0.2636364
## [64] 0.1657010 0.1422886 1.0000000 0.1601344 0.1835687 0.1401961 NaN
## [71] 0.5714286 NaN 0.1704142 0.4285714 0.3428571 NaN 1.0000000
## [78] 1.0000000 0.1860465 0.2040816 0.3055556 NaN NaN NaN
## [85] NaN NaN 0.1460674 0.1914324 0.1616162 0.1812421 0.2227414
## [92] 0.2025496 1.0000000 NaN 1.0000000 0.8571429 0.1292876 0.1424303
## [99] NaN 1.0000000 NaN 0.1674419 0.1152297 0.2133527 0.1229579
## [106] 0.1457696 0.1377649 NaN 0.1530398 NaN NaN 1.0000000
## [113] 0.4242424 NaN 1.0000000 NaN 1.0000000 0.1778607 1.0000000
## [120] 1.0000000 0.7500000 1.0000000 0.1503532 1.0000000 1.0000000 1.0000000
## [127] 1.0000000 0.2131148 0.1769802 1.0000000 1.0000000 0.1820480 0.1440000
## [134] NaN NaN 0.1298819 0.1716687 0.3052632 0.2970297 0.8000000
## [141] 1.0000000 0.2784810 0.1486068 0.3052632 0.1708185 NaN 0.1660900
## [148] 0.6666667 1.0000000 NaN 1.0000000 1.0000000 NaN 0.1870968
## [155] 1.0000000 0.1820480 0.6666667 0.3333333 0.2975207 0.2602740 0.1625282
## [162] 0.1758918 0.1437186 0.1039711 0.1696323 0.1467890 1.0000000 1.0000000
## [169] 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333 0.5000000 NaN
## [176] 0.3131313 NaN 0.1817027 0.6250000 0.6000000 NaN NaN
## [183] 0.1820480 0.1440000 0.4146341 0.6000000 NaN 0.1512605 NaN
## [190] 1.0000000 NaN NaN 0.1298819 NaN NaN NaN
## [197] 1.0000000 0.3972603 0.1540948 0.1098310 0.1569264 0.3902439 NaN
## [204] NaN 0.2186544 0.1527778 NaN 0.1227468 NaN NaN
## [211] NaN 0.1429990 0.1431412 0.1364486 0.1364486 0.1226415 1.0000000
## [218] 0.2338710 0.2210201 NaN NaN NaN 1.0000000 1.0000000
## [225] NaN 0.1956224 NaN NaN NaN 1.0000000 NaN
## [232] NaN 1.0000000 NaN 1.0000000 1.0000000 1.0000000 1.0000000
## [239] 1.0000000 1.0000000 1.0000000 0.7500000 1.0000000 1.0000000 1.0000000
## [246] NaN NaN NaN NaN 0.1096626 0.2417582 NaN
## [253] NaN 1.0000000 0.1550802 0.1822785 0.2363112 0.1429990 0.1099237
## [260] 1.0000000 NaN 0.1437126 NaN 0.2473118 NaN 0.8333333
## [267] NaN 0.1666667 0.1892247 0.6666667 NaN 0.6666667 NaN
## [274] NaN NaN 0.1509434 NaN 0.3125000 NaN 0.1820480
## [281] 0.1812421 NaN 1.0000000 NaN 0.3372093 NaN NaN
## [288] 1.0000000 NaN NaN NaN NaN NaN 0.6000000
## [295] NaN 1.0000000 NaN NaN NaN NaN 1.0000000
## [302] 1.0000000 NaN 1.0000000 NaN NaN NaN NaN
## [309] 0.3030303 NaN 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [316] 1.0000000 1.0000000 NaN 0.1959459 0.2442748 NaN NaN
## [323] 0.6666667 NaN NaN NaN NaN 1.0000000 0.1798742
## [330] 0.6666667 0.6666667 0.6666667 NaN NaN NaN NaN
## [337] NaN NaN NaN 1.0000000 NaN NaN NaN
## [344] NaN NaN NaN 0.1232759 0.1228669 0.1228669 NaN
## [351] NaN NaN 0.1104294 NaN 1.0000000 NaN NaN
## [358] NaN NaN 0.1892247 0.3075472 NaN NaN NaN
## [365] NaN NaN NaN NaN NaN 0.1237113 NaN
## [372] NaN NaN NaN NaN NaN NaN NaN
## [379] NaN NaN NaN NaN NaN NaN NaN
## [386] NaN NaN NaN NaN NaN NaN NaN
## [393] NaN NaN NaN NaN NaN NaN NaN
## [400] NaN NaN NaN NaN NaN NaN NaN
## [407] NaN NaN NaN NaN NaN NaN NaN
## [414] NaN NaN NaN NaN NaN NaN NaN
## [421] NaN NaN NaN NaN NaN NaN NaN
## [428] NaN NaN NaN NaN 0.2604736 NaN NaN
## [435] NaN NaN NaN NaN NaN NaN NaN
## [442] NaN NaN
##
## $centralization
## [1] NaN
##
## $theoretical_max
## [1] 441.0023centr_clo(DLT1_Networknoprof_ungrup)## $res
## [1] 0.4349315 0.3167082 0.2539683 0.3547486 0.3167082 0.3713450 0.3813814
## [8] 0.3670520 0.3537604 0.3802395 0.4738806 0.3883792 0.4070513 0.4136808
## [15] 0.3956386 0.2419048 0.3950617 0.3535912 0.4096774 0.2939815 NaN
## [22] 0.2634855 NaN 0.3956386 NaN 0.3143564 0.3607955 0.2770563
## [29] 0.4110032 0.4618182 NaN NaN NaN 0.4083601 0.4136808
## [36] 0.3547486 NaN 0.3023810 0.3587571 0.2606925 0.3498623 0.3577465
## [43] 0.3128079 0.4847328 0.2613169 0.2786177 0.3240506 0.2886364 0.3993711
## [50] 0.3423181 0.2995283 0.3451087 0.4006309 0.4006309 NaN 0.3714286
## [57] 1.0000000 0.3860182 0.2869955 0.4618182 0.3802395 0.3931889 0.3735294
## [64] 0.3290155 NaN 0.2273535 0.2313297 0.4319728 0.2098361 NaN
## [71] NaN 0.2406015 NaN 0.2953488 0.2879819 0.3105134 0.3075061
## [78] NaN 1.0000000 0.3333333 0.2313043 0.2986425 0.3450135 NaN
## [85] 0.2990654 1.0000000 1.0000000 0.3143564 NaN NaN 0.3413333
## [92] 0.3135802 0.2419660 NaN NaN 1.0000000 1.0000000 0.3618234
## [99] 0.3342037 0.2634855 0.2106136 0.2098361 0.2729167 0.3791045 0.2098361
## [106] NaN 0.2955556 0.3324675 0.3907692 0.2504931 0.2015748 NaN
## [113] NaN 0.3060241 NaN NaN NaN NaN 0.2273535
## [120] NaN 0.3200000 NaN 0.2770563 NaN NaN NaN
## [127] NaN 0.3105134 1.0000000 NaN 1.0000000 0.2300725 0.1887073
## [134] 0.1882353 0.2226027 0.3273196 0.3239796 0.2764579 0.2438095 NaN
## [141] NaN 0.3350923 NaN 0.3489011 NaN NaN 0.3669468
## [148] 0.3100233 NaN NaN NaN NaN NaN NaN
## [155] 0.3469945 0.2776573 1.0000000 0.2179054 NaN NaN 0.2988235
## [162] NaN 0.3368700 NaN 0.3282051 1.0000000 0.6666667 NaN
## [169] NaN 0.2419660 NaN NaN NaN NaN NaN
## [176] NaN 0.3075061 0.2365989 0.6666667 NaN NaN NaN
## [183] 0.3128079 0.3342105 0.3091787 NaN NaN NaN NaN
## [190] 0.2490272 0.3386667 0.3167082 NaN NaN 0.2946636 0.3413333
## [197] 0.2828947 0.3649425 0.3231552 NaN 0.3239796 0.3545706 0.3273196
## [204] 0.2480620 1.0000000 NaN 0.3198992 0.2500000 NaN 0.2438095
## [211] 0.3198992 0.2437620 NaN NaN 0.3160494 0.2912844 0.2912844
## [218] 0.2922374 0.2581301 0.2922374 0.2919540 0.2912844 0.2960373 0.2922374
## [225] 0.2922374 0.2922374 0.2912844 0.2922374 0.2419660 0.2433962 0.3100962
## [232] 0.2419660 NaN NaN NaN NaN NaN NaN
## [239] NaN NaN NaN NaN 0.2764579 NaN NaN
## [246] 0.2807018 0.2758621 0.2640333 0.2689076 NaN NaN NaN
## [253] NaN NaN NaN NaN NaN NaN 0.2490272
## [260] NaN NaN NaN NaN NaN NaN NaN
## [267] 0.2776573 0.2825607 NaN NaN NaN NaN NaN
## [274] 0.3282051 0.3350785 NaN 0.2665289 0.3282051 0.3307292 NaN
## [281] 1.0000000 0.2179054 NaN NaN NaN NaN 1.0000000
## [288] 1.0000000 0.2534653 1.0000000 1.0000000 NaN NaN NaN
## [295] NaN NaN 1.0000000 0.2298025 0.2273535 0.2280072 NaN
## [302] NaN NaN NaN NaN NaN 1.0000000 NaN
## [309] NaN NaN NaN NaN NaN NaN NaN
## [316] NaN NaN NaN NaN NaN 1.0000000 NaN
## [323] NaN NaN 0.1868613 0.1868613 0.1868613 NaN NaN
## [330] NaN NaN NaN 0.2764579 NaN NaN 1.0000000
## [337] NaN NaN NaN NaN 0.2423664 NaN NaN
## [344] NaN NaN NaN NaN NaN NaN NaN
## [351] NaN 0.1890694 0.3047619 0.1751026 NaN NaN 0.6666667
## [358] NaN 0.2101806 NaN 0.3216080 NaN NaN NaN
## [365] NaN NaN NaN NaN NaN NaN NaN
## [372] NaN NaN NaN NaN NaN NaN NaN
## [379] NaN NaN NaN NaN NaN NaN NaN
## [386] NaN NaN NaN NaN NaN NaN NaN
## [393] NaN NaN NaN NaN NaN NaN NaN
## [400] NaN NaN NaN NaN NaN NaN NaN
## [407] NaN NaN NaN NaN NaN NaN NaN
## [414] NaN NaN NaN NaN NaN NaN NaN
## [421] NaN 0.3167082 NaN NaN NaN NaN NaN
## [428] NaN NaN NaN NaN 0.4136808 NaN 0.2360595
## [435] NaN NaN 0.2933025 NaN 0.2656904 NaN NaN
## [442] NaN NaN
##
## $centralization
## [1] NaN
##
## $theoretical_max
## [1] 441.0023centr_clo(dlt1_network_grup)## $res
## [1] 0.3155172 NaN 0.2584746 0.3138937 0.3177083 0.3345455 0.3452830
## [8] 0.3321234 0.3078203 0.2517100 0.3485714 0.2731343 0.1833667 0.2802450
## [15] 0.3376384 NaN 0.3376384 0.2941176 0.3315217 NaN NaN
## [22] 0.2951613 NaN 0.3144330 0.3004926 0.2588402 0.2951613 NaN
## [29] 0.2389034 0.3267857 0.1936842 0.2239902 0.2980456 0.2633094 0.2965964
## [36] 0.3321234 0.1936508 NaN 0.3155172 NaN NaN NaN
## [43] NaN 0.3519231 0.2906793 0.2517100 NaN NaN 0.3188153
## [50] 0.3395176 0.3388889 0.2500000 0.2965964 0.3452830 0.2822086 NaN
## [57] 0.2401575 0.3363971 0.3388889 0.3479087 0.3333333 0.2906793 0.3395176
## [64] 0.3279570 0.2961165 0.2928000 0.2890995 0.3144330 0.3117547 NaN
## [71] 0.3401487 0.2909380 0.2473118 0.2739521 0.3160622 NaN 1.0000000
## [78] 0.2916006 0.2734027 0.2566248 0.3193717 0.2906793 0.3291367 NaN
## [85] 0.3291367 NaN 0.3433396 0.3546512 0.2628571 0.3122867 0.3333333
## [92] 0.2772727 0.2257669 0.2900158 0.3017945 0.3345324 0.2846271 0.3070470
## [99] 0.2756024 0.3297297 0.2764350 0.3315315 0.2946860 0.3139932 0.3034826
## [106] 0.2667638 0.3160622 NaN 0.3065327 0.2906793 NaN 0.3014827
## [113] 0.2584746 NaN 0.2918660 0.2904762 0.2259259 0.2614286 0.2259259
## [120] 0.2916006 0.3370166 0.2259259 0.2419562 0.2916006 0.2260442 0.2916006
## [127] 0.2916006 0.3472486 0.3182609 0.2260442 0.3014827 0.2541436 0.3097643
## [134] 0.2906793 NaN 0.2965964 0.3133562 0.3363971 0.3102867 0.2278325
## [141] 0.2914013 0.2932692 0.3036304 0.3070470 0.2541436 0.2758621 0.3036304
## [148] 0.2263223 0.2261614 0.2906793 0.2261614 0.2918660 NaN 0.3321234
## [155] 0.2771084 0.2541667 0.3345521 0.3171577 0.2914013 0.3011457 0.2666667
## [162] 0.3166090 0.2990196 0.2285714 0.3149742 0.2977346 0.2909380 0.2266010
## [169] 0.2920635 0.2920635 0.2920635 0.2928000 0.3044925 0.2343949 NaN
## [176] 0.3166090 NaN 0.3080808 0.3315508 0.2327910 0.2906793 NaN
## [183] 0.3016393 0.2613636 0.3285458 0.2929936 NaN 0.3050000 NaN
## [190] 0.3285458 NaN 0.2906793 0.3345521 0.2906793 0.2904762 0.2906793
## [197] 0.2764350 0.2914013 0.2322335 0.2914013 0.3117547 0.2934609 0.2904762
## [204] 0.2906793 0.2854914 0.3086003 NaN 0.2510288 NaN NaN
## [211] 0.2758621 0.3357664 0.2591549 0.2962963 0.2386511 0.2510288 0.2909380
## [218] 0.2538141 0.3388889 NaN 0.2906793 NaN 0.3297297 1.0000000
## [225] NaN 0.3426966 NaN NaN NaN 0.2261614 NaN
## [232] 0.3285714 1.0000000 NaN 0.2914013 0.2911392 1.0000000 1.0000000
## [239] 0.2911392 0.2911392 0.2911392 0.2927215 0.2911392 1.0000000 0.2922591
## [246] 0.2906793 0.3291367 0.2906793 0.3279570 0.3297297 0.1623780 0.2906793
## [253] 0.2900158 0.2486413 0.2982172 0.2715134 0.2500000 0.3357664 0.2920635
## [260] 0.2261614 0.2906793 0.2053571 NaN 0.2787879 0.2918660 0.2545202
## [267] 0.2906793 0.2570225 0.2682216 0.2262052 0.2906793 0.2483039 NaN
## [274] NaN NaN 0.3315217 NaN 0.2071669 0.2906793 0.3076923
## [281] 0.3009868 NaN 0.2261614 0.2906793 0.2603129 NaN NaN
## [288] 0.2263223 NaN NaN NaN 0.2904762 NaN 0.2269939
## [295] 0.2904762 0.2260442 0.2906793 0.3285714 0.2906793 0.2914013 0.2909380
## [302] 0.3009868 0.3285458 0.3011457 0.2909380 NaN 0.2751880 NaN
## [309] 0.2577031 NaN 1.0000000 1.0000000 1.0000000 0.2911392 0.2266010
## [316] 1.0000000 0.3297297 0.2904762 0.2793893 0.3339383 NaN 0.2906793
## [323] 0.2764350 NaN NaN 0.2758621 NaN 0.2486486 0.3505747
## [330] 0.2920635 0.2909380 0.2267157 0.2906793 NaN 0.2906793 0.2756024
## [337] 0.2906793 0.2909380 0.2914013 0.2946860 0.2760181 0.2909380 0.2923323
## [344] 0.2906793 0.2909380 0.2918660 0.3315217 0.2510232 0.2510232 0.2904762
## [351] 0.2751880 NaN 0.2500000 0.3285714 0.2909380 0.2904762 0.2906793
## [358] 0.2909380 NaN 0.3333333 0.3315315 0.2906793 0.2906793 0.2906793
## [365] 0.2906793 0.2906793 0.2906793 0.2906793 0.2906793 0.2386511 0.2906793
## [372] 0.2906793 0.2906793 0.2906793 0.2906793 0.3285714 0.3285714 0.3285714
## [379] 0.3285714 0.2906793 0.2906793 0.3285714 0.2906793 0.2906793 0.2906793
## [386] 0.2906793 0.2906793 0.3285714 0.2906793 0.2906793 0.2906793 0.2906793
## [393] 0.2906793 0.2906793 0.2906793 0.2906793 0.3285714 0.2906793 0.3285714
## [400] 0.2906793 0.2906793 0.2906793 0.3285714 0.2906793 0.2906793 0.3285714
## [407] 0.2906793 0.2906793 0.3285714 0.3285714 NaN 0.2906793 0.2900158
## [414] 0.2906793 0.2906793 0.2758621 0.2758621 0.2758621 0.2758621 0.2758621
## [421] 0.2758621 NaN 0.2758621 0.2906793 0.2906793 0.2906793 0.2906793
## [428] 0.2906793 0.2906793 0.2906793 0.2906793 0.3446328 0.2751880 NaN
## [435] NaN NaN NaN NaN NaN NaN NaN
## [442] NaN NaN 0.4075724 0.3788820
##
## $centralization
## [1] NaN
##
## $theoretical_max
## [1] 443.0022centr_clo(DLT1_Network_ungrup)## $res
## [1] 0.4581940 0.3223529 0.2555556 0.3881020 0.3712737 0.4005848 0.4126506
## [8] 0.3925501 0.3870056 0.4126506 0.4892857 0.3914286 0.4189602 0.4294671
## [15] 0.4029412 0.2723658 0.3976945 0.3641161 0.4321767 0.3051225 NaN
## [22] 0.3624339 NaN 0.4254658 NaN 0.3341463 0.3892045 0.2936170
## [29] 0.4335443 0.4707904 NaN NaN NaN 0.4065282 0.4241486
## [36] 0.3643617 NaN 0.3127854 0.3859155 0.2618596 0.3521851 0.3567708
## [43] 0.3200935 0.5000000 0.2624521 0.3059867 0.3415842 0.2896406 0.4176829
## [50] 0.3774105 0.3238771 0.3530928 0.3959538 0.4029412 NaN 0.3816156
## [57] 0.2565543 0.3870056 0.3584416 0.4691781 0.4114114 0.4189602 0.4041298
## [64] 0.3774105 NaN 0.2338983 0.3468354 0.4491803 0.2129630 0.3415842
## [71] 0.3484848 0.3458647 NaN 0.3163972 0.3171296 0.3653333 0.3702703
## [78] NaN 1.0000000 0.3374384 0.2469565 0.3256351 0.3502538 NaN
## [85] 0.2987013 1.0000000 1.0000000 0.3753425 NaN NaN 0.3538462
## [92] 0.3341463 0.2442478 NaN NaN 0.3432099 0.2565056 0.3948127
## [99] 0.3382353 0.2686275 0.3459596 0.2974138 0.3053097 0.3763736 0.2129630
## [106] NaN 0.3101124 0.3349515 0.3859155 0.2541744 0.2038405 NaN
## [113] NaN 0.3231132 NaN NaN NaN NaN 0.2338983
## [120] NaN 0.3760218 NaN 0.2929936 NaN NaN NaN
## [127] NaN 0.3163972 1.0000000 NaN 1.0000000 0.2362069 0.1926864
## [134] 0.1922006 0.2213376 0.3459596 0.3442211 0.2749004 0.2536765 NaN
## [141] NaN 0.3486005 0.3415842 0.3494898 NaN 0.3415842 0.3763441
## [148] 0.3381295 NaN NaN NaN NaN NaN NaN
## [155] 0.3753425 0.2936170 0.3502538 0.2171157 NaN NaN 0.3567708
## [162] 0.3415842 0.3494898 NaN 0.3349515 0.2899160 0.6666667 NaN
## [169] NaN 0.2442478 NaN NaN NaN NaN NaN
## [176] 0.3494898 0.3261905 0.2473118 0.6666667 NaN NaN NaN
## [183] 0.3317191 0.3753425 0.3293556 NaN NaN NaN NaN
## [190] 0.2754491 0.3399504 0.3208431 NaN NaN 0.3058036 0.3458647
## [197] 0.2989247 0.3743169 0.3732970 NaN 0.3442211 0.3833333 0.3341463
## [204] 0.2518248 0.3450000 NaN 0.3374384 0.2536765 0.2890295 0.2967742
## [211] 0.3643617 0.2965368 NaN NaN 0.3341404 0.3024283 0.3494898
## [218] 0.3030973 0.3512821 0.3032967 0.3030973 0.3024283 0.3064877 0.3032967
## [225] 0.3032967 0.3032967 0.3024283 0.3143508 0.2442478 0.2455830 0.3210162
## [232] 0.2442478 NaN NaN NaN NaN NaN NaN
## [239] NaN NaN NaN NaN 0.2751004 NaN NaN
## [246] 0.2851240 0.2799189 0.2691552 0.2890295 NaN NaN NaN
## [253] NaN NaN NaN NaN NaN NaN 0.2754491
## [260] NaN NaN 0.2878151 NaN NaN NaN NaN
## [267] 0.2936170 0.2799189 NaN NaN 0.3425000 NaN NaN
## [274] 0.3349515 0.3484848 NaN 0.2730845 0.3699732 0.3374384 NaN
## [281] 1.0000000 0.2169811 NaN NaN NaN NaN 1.0000000
## [288] 1.0000000 0.2569832 1.0000000 1.0000000 NaN 0.2890295 NaN
## [295] NaN NaN 1.0000000 0.2358974 0.2338983 0.2619503 NaN
## [302] NaN NaN NaN NaN NaN 1.0000000 NaN
## [309] NaN 0.3425000 NaN NaN NaN NaN NaN
## [316] NaN NaN NaN NaN NaN 1.0000000 NaN
## [323] NaN NaN 0.2085236 0.3441397 0.2087746 NaN 0.3415842
## [330] NaN NaN NaN 0.2749004 NaN NaN 0.3442211
## [337] NaN NaN NaN NaN 0.2442068 NaN NaN
## [344] NaN NaN NaN NaN NaN NaN NaN
## [351] NaN 0.2589118 0.3157895 0.2584270 NaN NaN 0.6666667
## [358] NaN 0.2132921 NaN 0.3247059 NaN NaN NaN
## [365] NaN NaN NaN NaN NaN NaN NaN
## [372] NaN NaN NaN NaN NaN NaN NaN
## [379] NaN NaN NaN NaN NaN NaN NaN
## [386] NaN NaN NaN NaN NaN NaN NaN
## [393] NaN NaN NaN NaN NaN NaN NaN
## [400] NaN NaN NaN NaN NaN NaN NaN
## [407] NaN NaN NaN NaN 0.3415842 NaN NaN
## [414] NaN NaN NaN NaN NaN NaN NaN
## [421] NaN 0.3200935 NaN NaN NaN NaN NaN
## [428] NaN NaN NaN NaN 0.4065282 NaN 0.2952586
## [435] 0.2887029 0.2887029 0.2984749 0.2896406 0.2807377 0.3415842 NaN
## [442] NaN NaN 0.5150376 0.4029412
##
## $centralization
## [1] NaN
##
## $theoretical_max
## [1] 443.0022#density
edge_density(DLT1_networknoprof_grup)## [1] 0.003610717edge_density(DLT1_Networknoprof_ungrup)## [1] 0.004713849edge_density(dlt1_network_grup)## [1] 0.006346796edge_density(DLT1_Network_ungrup)## [1] 0.006301245graph.density(DLT1_networknoprof_grup)## [1] 0.003610717graph.density(DLT1_Networknoprof_ungrup)## [1] 0.004713849graph.density(dlt1_network_grup)## [1] 0.006346796graph.density(DLT1_Network_ungrup)## [1] 0.006301245#reciprocity
reciprocity(DLT1_networknoprof_grup)## [1] 0.1490313reciprocity(DLT1_Networknoprof_ungrup)## [1] 0.2020202reciprocity(dlt1_network_grup)## [1] 0.1574151reciprocity(DLT1_Network_ungrup)## [1] 0.2054681#transitivity
transitivity(DLT1_networknoprof_grup)## [1] 0.1304transitivity(DLT1_Networknoprof_ungrup)## [1] 0.1664638transitivity(dlt1_network_grup)## [1] 0.0385422transitivity(DLT1_Network_ungrup)## [1] 0.1702297library(ggraph)
DLT1_networknoprof_grup |> centr_degree() |> as_tibble() |> select(centralization) |> unique()DLT1_Networknoprof_ungrup |> centr_degree() |> as_tibble() |> select(centralization) |> unique()dlt1_network_grup |> centr_degree() |> as_tibble() |> select(centralization) |> unique() DLT1_Network_ungrup |> centr_degree() |> as_tibble() |> select(centralization) |> unique()DLT1_networknoprof_grup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique()DLT1_Networknoprof_ungrup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique()dlt1_network_grup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique()DLT1_Network_ungrup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique()DLT1_networknoprof_grup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique()DLT1_Networknoprof_ungrup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique()dlt1_network_grup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique()DLT1_Network_ungrup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique()DLT1_networknoprof_grup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_Networknoprof_ungrup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()dlt1_network_grup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_Network_ungrup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_networknoprof_grup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_Networknoprof_ungrup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()dlt1_network_grup |> centr_betw()|> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_Network_ungrup |> centr_betw()|> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique()DLT1_networknoprof_grup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique()DLT1_Networknoprof_ungrup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique()dlt1_network_grup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique()DLT1_Network_ungrup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique()DLT1_networknoprof_grup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_Networknoprof_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()dlt1_network_grup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_Network_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_networknoprof_grup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_Networknoprof_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()dlt1_network_grup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_Network_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique()DLT1_networknoprof_grup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique()DLT1_Networknoprof_ungrup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique()dlt1_network_grup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique()DLT1_Network_ungrup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique()DLT1_networknoprof_grup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique()DLT1_Networknoprof_ungrup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique()dlt1_network_grup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique()DLT1_Network_ungrup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique()All relevant measures from above are merged into the nodelists for the networks.
library(dplyr)
NNPG1 <- DLT1_networknoprof_grup |> activate(nodes) |> mutate(out_degree = centrality_degree(mode = "out"))
NNPG2 <- NNPG1 |> activate(nodes) |> mutate(in_degree = centrality_degree(mode = "in"))
NNPG3 <- NNPG2 |> activate(nodes) |> mutate(centrality = centrality_degree(mode = "total"))
NNPG4 <- NNPG3 |> activate(nodes) |> mutate(betweenness = betweenness(NNPG3))
NNPG5 <- NNPG4 |> activate(nodes) |> mutate(closeness = closeness(NNPG4))
NNPG6 <- NNPG5 |> activate(nodes) |> mutate(edge_density = edge_density(NNPG5))
NNPG7 <- NNPG6 |> activate(nodes) |> mutate(graph_density = graph.density(NNPG6))
NNPG8 <- NNPG7 |> activate(nodes) |> mutate(reciprocity = reciprocity(NNPG7))
DLT1_networknoprof_grup_final <- NNPG8 |> activate(nodes) |> mutate(transitivity = transitivity(NNPG8))
NNPG1 <- DLT1_Networknoprof_ungrup |> activate(nodes) |> mutate(out_degree = centrality_degree(mode = "out"))
NNPG2 <- NNPG1 |> activate(nodes) |> mutate(in_degree = centrality_degree(mode = "in"))
NNPG3 <- NNPG2 |> activate(nodes) |> mutate(centrality = centrality_degree(mode = "total"))
NNPG4 <- NNPG3 |> activate(nodes) |> mutate(betweenness = betweenness(NNPG3))
NNPG5 <- NNPG4 |> activate(nodes) |> mutate(closeness = closeness(NNPG4))
NNPG6 <- NNPG5 |> activate(nodes) |> mutate(edge_density = edge_density(NNPG5))
NNPG7 <- NNPG6 |> activate(nodes) |> mutate(graph_density = graph.density(NNPG6))
NNPG8 <- NNPG7 |> activate(nodes) |> mutate(reciprocity = reciprocity(NNPG7))
DLT1_Networknoprof_ungrup_final <- NNPG8 |> activate(nodes) |> mutate(transitivity = transitivity(NNPG8))
NNPG1 <- dlt1_network_grup |> activate(nodes) |> mutate(out_degree = centrality_degree(mode = "out"))
NNPG2 <- NNPG1 |> activate(nodes) |> mutate(in_degree = centrality_degree(mode = "in"))
NNPG3 <- NNPG2 |> activate(nodes) |> mutate(centrality = centrality_degree(mode = "total"))
NNPG4 <- NNPG3 |> activate(nodes) |> mutate(betweenness = betweenness(NNPG3))
NNPG5 <- NNPG4 |> activate(nodes) |> mutate(closeness = closeness(NNPG4))
NNPG6 <- NNPG5 |> activate(nodes) |> mutate(edge_density = edge_density(NNPG5))
NNPG7 <- NNPG6 |> activate(nodes) |> mutate(graph_density = graph.density(NNPG6))
NNPG8 <- NNPG7 |> activate(nodes) |> mutate(reciprocity = reciprocity(NNPG7))
dlt1_network_grup_final <- NNPG8 |> activate(nodes) |> mutate(transitivity = transitivity(NNPG8))
NNPG1 <- DLT1_Network_ungrup |> activate(nodes) |> mutate(out_degree = centrality_degree(mode = "out"))
NNPG2 <- NNPG1 |> activate(nodes) |> mutate(in_degree = centrality_degree(mode = "in"))
NNPG3 <- NNPG2 |> activate(nodes) |> mutate(centrality = centrality_degree(mode = "total"))
NNPG4 <- NNPG3 |> activate(nodes) |> mutate(betweenness = betweenness(NNPG3))
NNPG5 <- NNPG4 |> activate(nodes) |> mutate(closeness = closeness(NNPG4))
NNPG6 <- NNPG5 |> activate(nodes) |> mutate(edge_density = edge_density(NNPG5))
NNPG7 <- NNPG6 |> activate(nodes) |> mutate(graph_density = graph.density(NNPG6))
NNPG8 <- NNPG7 |> activate(nodes) |> mutate(reciprocity = reciprocity(NNPG7))
DLT1_Network_ungrup_final <- NNPG8 |> activate(nodes) |> mutate(transitivity = transitivity(NNPG8))I calculate the mean and median out-degrees for the graphs and turn them into a .csv file.
mean_out1<- DLT1_networknoprof_grup_final |> activate(nodes) |> select(out_degree) |> as_tibble()
mean_out2<- DLT1_Networknoprof_ungrup_final |> activate(nodes) |> select(out_degree) |> as_tibble()
mean_out3<- dlt1_network_grup_final |> activate(nodes) |> select(out_degree) |> as_tibble()
mean_out4<- DLT1_Network_ungrup_final |> activate(nodes) |> select(out_degree) |> as_tibble()
?write_csv
out_degree_averages <- tibble(as_tibble_col(c("noprof_grup", "noprof_nogrup", "prof_grup", "prof_nogrup"), column_name = "dataset"), +
as_tibble_col(c(mean(mean_out1$out_degree), +
mean(mean_out2$out_degree), +
mean(mean_out3$out_degree), +
mean(mean_out4$out_degree)), column_name = "mean"), +
as_tibble_col(c(median(mean_out1$out_degree), +
median(mean_out2$out_degree), +
median(mean_out3$out_degree), +
median(mean_out4$out_degree)), column_name = "median"))
out_degree_averages |> write_csv(file = "outdegreeavg.csv")
read_csv("outdegreeavg.csv")## Rows: 4 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): dataset
## dbl (2): mean, median
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.It is clear above that the dataset with the most activity was the grouped dataset with the professor, but the grouped dataset without the professor has the least activity, a drop of 1.32. This indicates that the groups were far more dependent on the professor than on the students for interactions, which is to say that most interactions were student-professor rather than student-student. However, the mean interactions in the ungrouped dataset without the professor dropped by only .71. This smaller drop indicates less reliance on the professors. However, the ungrouped dataset without the professor also is the only one with a median number of interactions of 0. Since 0 is the minimum number of interactions, less than half of the students actually engaged. Thus, this dataset is dominated by a few highly active students, not many moderately active ones. Now, let’s get to graphing.
library(ggplot2)
library(ggraph)
DLT1_networknoprof_grup_final## # A tbl_graph: 443 nodes and 707 edges
## #
## # A directed multigraph with 158 components
## #
## # A tibble: 443 × 22
## UID Facilitator role1 experience experience2 grades location region country
## <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 0 libme… 1 6 to 10 secon… VA South US
## 2 2 0 class… 1 6 to 10 secon… FL South US
## 3 3 0 distr… 2 11 to 20 gener… PA North… US
## 4 4 0 class… 2 11 to 20 middle NC South US
## 5 5 0 other… 3 20+ gener… AL South US
## 6 6 0 class… 1 4 to 5 gener… AL South US
## # ℹ 437 more rows
## # ℹ 13 more variables: group <chr>, gender <chr>, expert <chr>, connect <chr>,
## # out_degree <dbl>, in_degree <dbl>, centrality <dbl>, betweenness <dbl>,
## # closeness <dbl>, edge_density <dbl>, graph_density <dbl>,
## # reciprocity <dbl>, transitivity <dbl>
## #
## # A tibble: 707 × 6
## from to Timestamp `Discussion Category` `Comment ID` `Discussion ID`
## <int> <int> <chr> <chr> <dbl> <chr>
## 1 355 356 4/4/13 20:12 Group D-L 10 3
## 2 19 310 4/4/13 23:13 Group A-C 16 9
## 3 19 4 4/4/13 23:35 Group N 19 7
## # ℹ 704 more rowsDLT1_Networknoprof_ungrup_final## # A tbl_graph: 443 nodes and 923 edges
## #
## # A directed multigraph with 213 components
## #
## # A tibble: 443 × 22
## UID Facilitator role1 experience experience2 grades location region country
## <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 0 libme… 1 6 to 10 secon… VA South US
## 2 2 0 class… 1 6 to 10 secon… FL South US
## 3 3 0 distr… 2 11 to 20 gener… PA North… US
## 4 4 0 class… 2 11 to 20 middle NC South US
## 5 5 0 other… 3 20+ gener… AL South US
## 6 6 0 class… 1 4 to 5 gener… AL South US
## # ℹ 437 more rows
## # ℹ 13 more variables: group <chr>, gender <chr>, expert <chr>, connect <chr>,
## # out_degree <dbl>, in_degree <dbl>, centrality <dbl>, betweenness <dbl>,
## # closeness <dbl>, edge_density <dbl>, graph_density <dbl>,
## # reciprocity <dbl>, transitivity <dbl>
## #
## # A tibble: 923 × 6
## from to Timestamp `Discussion Category` `Comment ID` `Discussion ID`
## <int> <int> <chr> <chr> <dbl> <chr>
## 1 216 19 4/4/13 23:21 Unit 1 Expert Panel 17 10
## 2 217 19 4/5/13 1:11 Unit 1 Expert Panel 24 11
## 3 218 19 4/5/13 13:26 Unit 1 Expert Panel 30 11
## # ℹ 920 more rowsdlt1_network_grup_final## # A tbl_graph: 445 nodes and 1254 edges
## #
## # A directed multigraph with 59 components
## #
## # A tibble: 445 × 22
## UID Facilitator role1 experience experience2 grades location region country
## <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 0 libme… 1 6 to 10 secon… VA South US
## 2 2 0 class… 1 6 to 10 secon… FL South US
## 3 3 0 distr… 2 11 to 20 gener… PA North… US
## 4 4 0 class… 2 11 to 20 middle NC South US
## 5 5 0 other… 3 20+ gener… AL South US
## 6 6 0 class… 1 4 to 5 gener… AL South US
## # ℹ 439 more rows
## # ℹ 13 more variables: group <chr>, gender <chr>, expert <chr>, connect <chr>,
## # out_degree <dbl>, in_degree <dbl>, centrality <dbl>, betweenness <dbl>,
## # closeness <dbl>, edge_density <dbl>, graph_density <dbl>,
## # reciprocity <dbl>, transitivity <dbl>
## #
## # A tibble: 1,254 × 6
## from to Timestamp `Discussion Category` `Comment ID` `Discussion ID`
## <int> <int> <chr> <chr> <dbl> <chr>
## 1 360 444 4/4/13 16:32 Group N 2 2
## 2 356 444 4/4/13 18:45 Group D-L 3 1
## 3 356 444 4/4/13 18:47 Group D-L 4 3
## # ℹ 1,251 more rowsDLT1_Network_ungrup_final## # A tbl_graph: 445 nodes and 1245 edges
## #
## # A directed multigraph with 203 components
## #
## # A tibble: 445 × 22
## UID Facilitator role1 experience experience2 grades location region country
## <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 0 libme… 1 6 to 10 secon… VA South US
## 2 2 0 class… 1 6 to 10 secon… FL South US
## 3 3 0 distr… 2 11 to 20 gener… PA North… US
## 4 4 0 class… 2 11 to 20 middle NC South US
## 5 5 0 other… 3 20+ gener… AL South US
## 6 6 0 class… 1 4 to 5 gener… AL South US
## # ℹ 439 more rows
## # ℹ 13 more variables: group <chr>, gender <chr>, expert <chr>, connect <chr>,
## # out_degree <dbl>, in_degree <dbl>, centrality <dbl>, betweenness <dbl>,
## # closeness <dbl>, edge_density <dbl>, graph_density <dbl>,
## # reciprocity <dbl>, transitivity <dbl>
## #
## # A tibble: 1,245 × 6
## from to Timestamp `Discussion Category` `Comment ID` `Discussion ID`
## <int> <int> <chr> <chr> <dbl> <chr>
## 1 310 444 4/4/13 20:34 Unit 1 Expert Panel 13 8
## 2 216 19 4/4/13 23:21 Unit 1 Expert Panel 17 10
## 3 217 444 4/5/13 0:58 Unit 1 Expert Panel 22 8
## # ℹ 1,242 more rowsggraph(DLT1_networknoprof_grup_final, layout = "fr") +
geom_edge_link(alpha = .2) +
geom_node_point(aes(color = group,
size = local_size())) +
theme_graph()## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggraph(DLT1_Networknoprof_ungrup_final, layout = "fr") +
geom_edge_link(alpha = .2) +
geom_node_point(aes(color = group,
size = local_size())) +
theme_graph()
ggraph(dlt1_network_grup_final, layout = "fr") +
geom_edge_link(alpha = .2) +
geom_node_point(aes(color = group,
size = local_size())) +
theme_graph()
ggraph(DLT1_Network_ungrup_final, layout = "fr") +
geom_edge_link(alpha = .2) +
geom_node_point(aes(color = group,
size = local_size())) +
theme_graph()
In the graphs above, it becomes clear that the grouped datasets show signs of clustering, though when the professor is present, that clustering is difficult to discern. In the grouped professor dataset, groups AC, N, and UZ are easily distinguishable, OT and M are harder to make out, but they are visible. Group DL, however, is really only visible if one knows what they are looking for. Without the professor, only OT is not immediately evident. In the ungrouped datasets, however, no clusters become evident. In both cases, the number of students who made no interactions once the professor was removed from the course grows, though this phenomenon expressed itself more in the grouped dataset. These findings reinforce those with the average mean and median.
DLT1_networknoprof_grup |> centr_degree() |> as_tibble() |> select(centralization) |> unique() -> A1
DLT1_Networknoprof_ungrup |> centr_degree() |> as_tibble() |> select(centralization) |> unique() -> A2
dlt1_network_grup |> centr_degree() |> as_tibble() |> select(centralization) |> unique() -> A3
DLT1_Network_ungrup |> centr_degree() |> as_tibble() |> select(centralization) |> unique() -> A4
DLT1_networknoprof_grup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique() -> B1
DLT1_Networknoprof_ungrup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique() -> B2
dlt1_network_grup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique() -> B3
DLT1_Network_ungrup |> centr_degree(mode = "out") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'out-degree') |> unique() -> B4
DLT1_networknoprof_grup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique() -> C1
DLT1_Networknoprof_ungrup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique() -> C2
dlt1_network_grup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique() -> C3
DLT1_Network_ungrup |> centr_degree(mode = "in") |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'in-degree') |> unique() -> C4
DLT1_networknoprof_grup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique() -> D1
DLT1_Networknoprof_ungrup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique() -> D2
dlt1_network_grup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique() -> D3
DLT1_Network_ungrup |> centr_betw() |> as_tibble() |> select(centralization) |> rename_at('centralization', ~'betweenness') |> unique() -> D4
DLT1_networknoprof_grup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique() -> F1
DLT1_Networknoprof_ungrup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique() -> F2
dlt1_network_grup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique() -> F3
DLT1_Network_ungrup |> edge_density() |> as_tibble() |> select(value) |>rename_at('value', ~'edge_density') |> unique() -> F4
DLT1_networknoprof_grup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique() -> G1
DLT1_Networknoprof_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique() -> G2
dlt1_network_grup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique() -> G3
DLT1_Network_ungrup |> graph.density() |> as_tibble() |> select(value) |>rename_at('value', ~'graph_density') |> unique() -> G4
DLT1_networknoprof_grup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique() -> I1
DLT1_Networknoprof_ungrup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique() -> I2
dlt1_network_grup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique() -> I3
DLT1_Network_ungrup |> reciprocity() |> as_tibble() |> select(value) |>rename_at('value', ~'reciprocity') |> unique() -> I4
DLT1_networknoprof_grup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique() -> J1
DLT1_Networknoprof_ungrup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique() -> J2
dlt1_network_grup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique() -> J3
DLT1_Network_ungrup |> transitivity() |> as_tibble() |> select(value) |>rename_at('value', ~'transitivity') |> unique() -> J4
?as_tibble_col
network_metrics <- tibble(as_tibble_col(c("noprof_grup", "noprof_nogrup", "prof_grup", "prof_nogrup"), column_name = "dataset"), as_tibble_col(c(A1, A2, A3, A4), column_name = "centralization"), as_tibble_col(c(B1, B2, B3, B4), column_name = "out-degree"), as_tibble_col(c(C1, C2, C3, C4), column_name = "in-degree"), as_tibble_col(c(D1, D2, D3, D4), column_name = "betweenness"), as_tibble_col(c(F1, F2, F3, F4), column_name = "edge_density"), as_tibble_col(c(G1, G2, G3, G4), column_name = "graph_density"), as_tibble_col(c(I1, I2, I3, I4), column_name = "reciprocity"), as_tibble_col(c(J1, J2, J3, J4), column_name = "transitivity"))
network_metrics |> write_csv(file = "network_metrics.csv")tibble(as_tibble_col(c("noprof_grup", "noprof_nogrup", "prof_grup", "prof_nogrup"), column_name = "dataset"), as_tibble_col(c(ei_index(DLT1_networknoprof_grup_final, node_attr_name = "group"), ei_index(DLT1_Networknoprof_ungrup_final, node_attr_name = "group"), ei_index(dlt1_network_grup_final , node_attr_name = "group"), ei_index(DLT1_Network_ungrup_final, node_attr_name = "group")), column_name = "homophily"))## Warning: `g` is multiplex.
## Warning: `g` is multiplex.
## Warning: `g` is multiplex.
## Warning: `g` is multiplex.