如何获得介于 0 和 1 之间的接近度分数
How to obtain a closeness score bounded between 0 and 1
我想计算网络的紧密度，例如
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)
通过使用包 igraph。使用函数 closeness 我得到：
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
我想获得一个介于 0 和 1 之间的归一化接近度分数。如果一个节点是孤立的，则为 0，如果一个节点直接连接所有其他节点，则为 1。我能做什么
在函数中，您可以调用“normalized = TRUE”，因为它默认为“FALSE”。旁注：如果您添加用于计算接近度的代码，将会很有帮助。
closeness(
graph,
vids = V(graph),
mode = c("out", "in", "all", "total"),
weights = NULL,
normalized = TRUE
)
您是否尝试过自己从 igraph 中对其进行标准化？您可以使用 caret 包：
library(caret)
c1 <-closeness(g)
df <- tibble(actors = actors,
close = c1)
df_normalized <- df %>%
preProcess(., method=c("range")) %>%
predict(., df)
df_normalized
我想计算网络的紧密度，例如
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)
通过使用包 igraph。使用函数 closeness 我得到：
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
我想获得一个介于 0 和 1 之间的归一化接近度分数。如果一个节点是孤立的，则为 0，如果一个节点直接连接所有其他节点，则为 1。我能做什么
在函数中，您可以调用“normalized = TRUE”，因为它默认为“FALSE”。旁注：如果您添加用于计算接近度的代码，将会很有帮助。
closeness(
graph,
vids = V(graph),
mode = c("out", "in", "all", "total"),
weights = NULL,
normalized = TRUE
)
您是否尝试过自己从 igraph 中对其进行标准化？您可以使用 caret 包：
library(caret)
c1 <-closeness(g)
df <- tibble(actors = actors,
close = c1)
df_normalized <- df %>%
preProcess(., method=c("range")) %>%
predict(., df)
df_normalized