I would like to calculate the closeness of a network like e.g.
relations=structure(list(From = c("France", "Italy", "Malta", "Spain",
"Germany", "Austria", "Luxembourg", "Luxembourg", "Luxembourg",
"Greece", "Slovakia", "Belgium", "Luxembourg", "Estonia", "Germany",
"Netherlands", "Netherlands", "Cyprus", "Greece", "Lithuania",
"Estonia", "Greece", "Lithuania", "Slovakia", "Netherlands",
"Luxembourg", "Estonia", "Malta", "Belgium", "Cyprus", "Austria",
"Estonia", "Austria", "Germany", "Austria", "Netherlands", "Portugal",
"Portugal", "Estonia", "Italy", "Spain", "Finland", "Belgium",
"Spain", "Estonia", "Latvia", "Luxembourg", "Luxembourg", "Netherlands",
"Italy", "Lithuania", "Cyprus", "Ireland", "Luxembourg", "Ireland",
"Spain", "Belgium", "Latvia", "Netherlands", "Italy", "Slovenia",
"Netherlands", "Greece", "Spain", "Austria", "Finland", "Malta",
"Spain", "Austria", "Lithuania", "France", "Portugal", "Cyprus",
"Finland", "Spain", "Spain", "Belgium", "Germany", "Germany",
"Greece", "Netherlands", "Luxembourg", "Estonia", "Ireland",
"Italy", "Portugal", "Greece", "Estonia", "Belgium", "Germany",
"Slovenia", "Slovakia", "Lithuania", "Slovenia", "Portugal",
"Portugal", "Slovakia", "France", "Portugal", "Netherlands"),
To = c("Slovakia", "Germany", "Portugal", "Austria", "Latvia",
"Cyprus", "Portugal", "Greece", "Italy", "Slovenia", "Ireland",
"Malta", "Ireland", "Germany", "Cyprus", "Portugal", "Slovenia",
"Italy", "Luxembourg", "France", "Slovakia", "Netherlands",
"Greece", "France", "Ireland", "Netherlands", "Cyprus", "Germany",
"Portugal", "Austria", "Luxembourg", "Austria", "Spain",
"Netherlands", "Belgium", "Cyprus", "Cyprus", "Luxembourg",
"Finland", "Belgium", "Lithuania", "Austria", "Spain", "Slovenia",
"Luxembourg", "Finland", "Slovenia", "Germany", "Austria",
"Slovenia", "Slovenia", "Portugal", "Finland", "Lithuania",
"Latvia", "France", "Netherlands", "Cyprus", "Spain", "Malta",
"France", "Finland", "Belgium", "Latvia", "Slovenia", "Slovenia",
"Cyprus", "Slovakia", "Slovakia", "Latvia", "Austria", "Ireland",
"Luxembourg", "Belgium", "Italy", "Estonia", "Greece", "Slovakia",
"Belgium", "Italy", "Latvia", "Malta", "Greece", "France",
"Greece", "Netherlands", "France", "Slovenia", "Latvia",
"Finland", "Austria", "Slovenia", "Belgium", "Cyprus", "Greece",
"Slovenia", "Cyprus", "Finland", "Malta", "Germany"), weight = c(3.63689639539779e-09,
2.36155345798604e-11, 1.90981717262912e-08, 1.27550515412245e-09,
2.75032212941667e-09, 3.79663088493923e-08, 1.42368280746262e-08,
1.51146811601353e-08, 1.75314892144034e-09, 2.07956027448449e-08,
1.38623943876156e-07, 1.92893849444567e-08, 5.66419694274739e-08,
5.8089561312222e-09, 1.95827364947702e-09, 6.7071674411728e-10,
4.23667746162198e-09, 4.62966220873406e-08, 1.21403390886489e-07,
1.26979107479853e-08, 5.59978765605208e-07, 3.14788992090851e-09,
5.0058794053616e-07, 5.22186410794211e-09, 9.21607337940845e-10,
2.51322812227745e-09, 3.04984607426863e-07, 3.20992561385791e-09,
8.92180336424503e-10, 6.12766236972513e-08, 1.20231538847898e-08,
1.34589665048046e-07, 8.84238627642763e-10, 2.63370671763944e-11,
1.11903749128791e-09, 1.16731679208085e-08, 4.99654438989995e-08,
2.90162442064053e-08, 2.55621731534268e-09, 1.58298464637584e-10,
5.07475932446428e-08, 2.54927518867625e-09, 2.24427326793633e-10,
4.49723284363684e-09, 2.43010060636845e-06, 1.90760030501003e-08,
6.35902610582491e-08, 5.48833215255332e-10, 4.06612010446768e-10,
5.32889353797193e-10, 1.23745070305762e-06, 8.89608483306497e-07,
3.75876719292064e-09, 4.44758324449273e-07, 1.35536579831994e-07,
5.42265922046125e-11, 4.8233448207134e-11, 3.4286615533894e-07,
2.0214285544428e-10, 8.57988303618593e-10, 1.35631258148319e-09,
7.97944858545598e-10, 4.2673026648725e-09, 9.28491399244895e-08,
1.03420112425729e-09, 2.4972909387896e-08, 8.12118760999133e-07,
1.31225365800221e-08, 1.77287287047586e-09, 6.53958727055997e-09,
3.05841917976996e-10, 9.20687775127977e-09, 4.32323785012335e-07,
8.40653865200987e-10, 1.23500809608508e-10, 6.59008808575339e-08,
1.13962199159936e-09, 4.68300414991578e-10, 3.54758342726504e-11,
6.55880483863129e-10, 1.17833628272869e-08, 5.08802989644665e-07,
7.22191649442396e-07, 2.7028184600344e-10, 2.2810918951755e-10,
8.10987753872438e-10, 1.50406384816011e-09, 5.46176491471454e-06,
2.32716789835402e-08, 2.10873259303399e-10, 1.81735402994258e-09,
9.71369590422109e-09, 2.01941765282435e-08, 1.8961914804496e-07,
9.4461402995167e-09, 1.69816623519093e-07, 9.16774899583353e-08,
8.54588845128718e-10, 4.79160495478785e-08, 2.31994460360924e-11
)), row.names = c(NA, -100L), class = c("tbl_df", "tbl",
"data.frame"))
actors= c("Slovakia", "Germany", "Portugal", "Austria", "Latvia", "Cyprus",
"Greece", "Italy", "Slovenia", "Ireland", "Malta", "Luxembourg",
"France", "Netherlands", "Spain", "Belgium", "Finland", "Lithuania",
"Estonia")
g <- graph_from_data_frame(relations, directed=TRUE, vertices=actors)
by using package igraph. Using function closeness I obtain:
closeness(g)
Slovakia Germany Portugal Austria Latvia Cyprus Greece Italy Slovenia Ireland
4002492 7111002 6478738 6502665 2017834 1029940 6615891 7164685 5533119 6240785
Malta Luxembourg France Netherlands Spain Belgium Finland Lithuania Estonia
5071100 7243749 6342911 7121291 7144000 7090895 6415382 3086085 7411727
I would like to obtain a normalized closeness score bounded between 0 and 1. It is 0 if a node is an isolate, and 1 if a node is directly connected all others. What can I do
CodePudding user response：
In the function, you can call for "normalized = TRUE" because it is "FALSE" by default. Sidenote: It is helpful if you add the code you used to calculate closeness.
closeness(
graph,
vids = V(graph),
mode = c("out", "in", "all", "total"),
weights = NULL,
normalized = TRUE
)
CodePudding user response：
Have you tried normalizing it yourself out of igraph? You might use caret package:
library(caret)
c1 <-closeness(g)
df <- tibble(actors = actors,
close = c1)
df_normalized <- df %>%
preProcess(., method=c("range")) %>%
predict(., df)
df_normalized