Distances to and from a vertex

Distances to and from a vertex

v_distance(
  x,
  mode = c("all", "out", "in"),
  weights = NULL,
  count_unnconnected = FALSE
)

Arguments

x

input graph

mode

character, how to follow edge direction: "all", "out", "in". Irrelevant for undirected graphs

weights

if not NULL, the name of an edge attribute to use as edge weights

count_unnconnected

logical, should unconnected vertices be included in the path lengths?

Value

data frame

Details

For each vertex, this function returns the average distance to all other vertices, the standard deviation of the distances, the maximum distance, the median distance, and the smmed distances.

The distances are determined either following the direction of the edges ( "out" or "in")–in case of a directed network, or irrespective of edge direction.

Distances can be weighted against an edge attribute.

When the graph is not connected, it is not obvious how to include the distances to/from unconnected vertices. If count_unnconnected is TRUE, the distances will count as infinite and most of the statistics will hence be infinite too. If count_unnconnected is TRUE, the paths to and from unconnected vertices are not included. Isolates will then have zero and NA scores, obviously.

Examples

data("florentine", package = "snafun")
g <- florentine$flobusiness
v_distance(g, mode = "all", count_unnconnected = FALSE)
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28
v_distance(g, mode = "all", count_unnconnected = TRUE)
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli       Inf    Inf   NaN Inf Inf
#> Albizzi          Inf    Inf   NaN Inf Inf
#> Barbadori        Inf    2.0   NaN Inf Inf
#> Bischeri         Inf    3.5   NaN Inf Inf
#> Castellani       Inf    2.5   NaN Inf Inf
#> Ginori           Inf    2.5   NaN Inf Inf
#> Guadagni         Inf    4.5   NaN Inf Inf
#> Lamberteschi     Inf    3.5   NaN Inf Inf
#> Medici           Inf    2.5   NaN Inf Inf
#> Pazzi            Inf    3.5   NaN Inf Inf
#> Peruzzi          Inf    2.5   NaN Inf Inf
#> Pucci            Inf    Inf   NaN Inf Inf
#> Ridolfi          Inf    Inf   NaN Inf Inf
#> Salviati         Inf    3.5   NaN Inf Inf
#> Strozzi          Inf    Inf   NaN Inf Inf
#> Tornabuoni       Inf    3.5   NaN Inf Inf
v_distance(g, mode = "in", count_unnconnected = FALSE)
#> Warning: The graph is not fully weakly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28
v_distance(g, mode = "out", count_unnconnected = FALSE)
#> Warning: The graph is not fully weakly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28
v_distance(snafun::to_network(g), mode = "all", count_unnconnected = FALSE)
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28
v_distance(snafun::to_network(g), mode = "all", count_unnconnected = TRUE)
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli       Inf    Inf   NaN Inf Inf
#> Albizzi          Inf    Inf   NaN Inf Inf
#> Barbadori        Inf    2.0   NaN Inf Inf
#> Bischeri         Inf    3.5   NaN Inf Inf
#> Castellani       Inf    2.5   NaN Inf Inf
#> Ginori           Inf    2.5   NaN Inf Inf
#> Guadagni         Inf    4.5   NaN Inf Inf
#> Lamberteschi     Inf    3.5   NaN Inf Inf
#> Medici           Inf    2.5   NaN Inf Inf
#> Pazzi            Inf    3.5   NaN Inf Inf
#> Peruzzi          Inf    2.5   NaN Inf Inf
#> Pucci            Inf    Inf   NaN Inf Inf
#> Ridolfi          Inf    Inf   NaN Inf Inf
#> Salviati         Inf    3.5   NaN Inf Inf
#> Strozzi          Inf    Inf   NaN Inf Inf
#> Tornabuoni       Inf    3.5   NaN Inf Inf
v_distance(snafun::to_network(g), mode = "in", count_unnconnected = FALSE)
#> Warning: The graph is not fully weakly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28
v_distance(snafun::to_network(g), mode = "out", count_unnconnected = FALSE)
#> Warning: The graph is not fully weakly connected, interpret result with prudence.
#>              average median stdev max sum
#> Acciaiuoli      0.00      0    NA   0   0
#> Albizzi         0.00      0    NA   0   0
#> Barbadori       1.06      2  0.82   3  17
#> Bischeri        1.56      2  1.42   4  25
#> Castellani      1.25      2  0.98   3  20
#> Ginori          1.38      2  1.10   4  22
#> Guadagni        2.00      3  1.81   5  32
#> Lamberteschi    1.50      2  1.47   4  24
#> Medici          1.19      1  1.19   4  19
#> Pazzi           1.75      2  1.44   5  28
#> Peruzzi         1.19      2  1.01   3  19
#> Pucci           0.00      0    NA   0   0
#> Ridolfi         0.00      0    NA   0   0
#> Salviati        1.75      2  1.44   5  28
#> Strozzi         0.00      0    NA   0   0
#> Tornabuoni      1.75      2  1.44   5  28

data(emon, package = "network")
g_n <- emon$LakePomona
v_distance(g_n, mode = "all")
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 0.95    1.0  0.22   1  19
#> Osage.County.Civil.Defense.Office                1.30    1.0  0.57   2  26
#> Osage.Country.Coroners.Office                    1.35    1.0  0.59   2  27
#> Osage.County.Attorneys.Office                    1.45    1.5  0.60   2  29
#> Kansas.State.Highway.Patrol                      0.95    1.0  0.22   1  19
#> Kansas.State.Parks.and.Resources.Authority       1.20    1.0  0.52   2  24
#> Kansas.State.Game.and.Fish.Commission            1.15    1.0  0.49   2  23
#> Kansas.State.Department.of.Transportation        1.70    2.0  0.57   2  34
#> US.Army.Corps.of.Engineers                       1.30    1.0  0.57   2  26
#> US.Army.Reserve                                  1.60    2.0  0.60   2  32
#> Crable.Ambulance                                 1.45    1.5  0.60   2  29
#> Franklin.County.Ambulance                        1.50    2.0  0.61   2  30
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.65    2.0  0.59   2  33
#> Shawnee.County.CD.Underwater.Rescue.Team         1.20    1.0  0.52   2  24
#> Burlingame.Police.Department                     1.10    1.0  0.45   2  22
#> Lyndon.Police.Department                         1.50    2.0  0.61   2  30
#> American.Red.Cross                               1.25    1.0  0.55   2  25
#> Topeka.Fire.Department.Rescue.No.1               1.65    2.0  0.59   2  33
#> Carbondale.Fire.Department                       1.55    2.0  0.60   2  31
#> Topeka.Radiator.and.Body.Works                   1.60    2.0  0.60   2  32
v_distance(g_n, mode = "in")
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 0.95    1.0  0.22   1  19
#> Osage.County.Civil.Defense.Office                1.60    2.0  0.60   2  32
#> Osage.Country.Coroners.Office                    1.35    1.0  0.59   2  27
#> Osage.County.Attorneys.Office                    1.65    2.0  0.59   2  33
#> Kansas.State.Highway.Patrol                      0.95    1.0  0.22   1  19
#> Kansas.State.Parks.and.Resources.Authority       1.25    1.0  0.55   2  25
#> Kansas.State.Game.and.Fish.Commission            1.25    1.0  0.55   2  25
#> Kansas.State.Department.of.Transportation        0.00    0.0    NA   0   0
#> US.Army.Corps.of.Engineers                       1.35    1.0  0.59   2  27
#> US.Army.Reserve                                  1.80    2.0  0.70   3  36
#> Crable.Ambulance                                 1.55    2.0  0.60   2  31
#> Franklin.County.Ambulance                        1.65    2.0  0.59   2  33
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.70    2.0  0.57   2  34
#> Shawnee.County.CD.Underwater.Rescue.Team         1.55    2.0  0.60   2  31
#> Burlingame.Police.Department                     1.80    2.0  0.52   2  36
#> Lyndon.Police.Department                         1.60    2.0  0.60   2  32
#> American.Red.Cross                               1.40    1.0  0.60   2  28
#> Topeka.Fire.Department.Rescue.No.1               2.30    2.5  0.86   3  46
#> Carbondale.Fire.Department                       0.00    0.0    NA   0   0
#> Topeka.Radiator.and.Body.Works                   1.70    2.0  0.57   2  34
v_distance(g_n, mode = "out")
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 1.15    1.0  0.57   2  23
#> Osage.County.Civil.Defense.Office                1.25    1.0  0.61   2  25
#> Osage.Country.Coroners.Office                    1.40    2.0  0.70   3  28
#> Osage.County.Attorneys.Office                    1.30    1.5  0.62   2  26
#> Kansas.State.Highway.Patrol                      1.10    1.0  0.55   2  22
#> Kansas.State.Parks.and.Resources.Authority       1.25    1.0  0.61   2  25
#> Kansas.State.Game.and.Fish.Commission            1.35    1.5  0.71   3  27
#> Kansas.State.Department.of.Transportation        1.65    2.0  0.65   3  33
#> US.Army.Corps.of.Engineers                       1.40    2.0  0.70   3  28
#> US.Army.Reserve                                  1.65    2.0  0.62   3  33
#> Crable.Ambulance                                 1.40    2.0  0.70   3  28
#> Franklin.County.Ambulance                        1.55    2.0  0.67   3  31
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.70    2.0  0.68   3  34
#> Shawnee.County.CD.Underwater.Rescue.Team         1.15    1.0  0.57   2  23
#> Burlingame.Police.Department                     0.90    1.0  0.34   2  18
#> Lyndon.Police.Department                         1.45    2.0  0.70   3  29
#> American.Red.Cross                               1.25    1.0  0.61   2  25
#> Topeka.Fire.Department.Rescue.No.1               1.55    2.0  0.67   3  31
#> Carbondale.Fire.Department                       1.50    2.0  0.69   3  30
#> Topeka.Radiator.and.Body.Works                   1.45    2.0  0.61   2  29
g_i <- snafun::to_igraph(g_n)
v_distance(g_i, mode = "all")
#> Warning: The graph is not fully strongly connected, interpret result with prudence.
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 0.95    1.0  0.22   1  19
#> Osage.County.Civil.Defense.Office                1.30    1.0  0.57   2  26
#> Osage.Country.Coroners.Office                    1.35    1.0  0.59   2  27
#> Osage.County.Attorneys.Office                    1.45    1.5  0.60   2  29
#> Kansas.State.Highway.Patrol                      0.95    1.0  0.22   1  19
#> Kansas.State.Parks.and.Resources.Authority       1.20    1.0  0.52   2  24
#> Kansas.State.Game.and.Fish.Commission            1.15    1.0  0.49   2  23
#> Kansas.State.Department.of.Transportation        1.70    2.0  0.57   2  34
#> US.Army.Corps.of.Engineers                       1.30    1.0  0.57   2  26
#> US.Army.Reserve                                  1.60    2.0  0.60   2  32
#> Crable.Ambulance                                 1.45    1.5  0.60   2  29
#> Franklin.County.Ambulance                        1.50    2.0  0.61   2  30
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.65    2.0  0.59   2  33
#> Shawnee.County.CD.Underwater.Rescue.Team         1.20    1.0  0.52   2  24
#> Burlingame.Police.Department                     1.10    1.0  0.45   2  22
#> Lyndon.Police.Department                         1.50    2.0  0.61   2  30
#> American.Red.Cross                               1.25    1.0  0.55   2  25
#> Topeka.Fire.Department.Rescue.No.1               1.65    2.0  0.59   2  33
#> Carbondale.Fire.Department                       1.55    2.0  0.60   2  31
#> Topeka.Radiator.and.Body.Works                   1.60    2.0  0.60   2  32
v_distance(g_i, mode = "in")
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 0.95    1.0  0.22   1  19
#> Osage.County.Civil.Defense.Office                1.60    2.0  0.60   2  32
#> Osage.Country.Coroners.Office                    1.35    1.0  0.59   2  27
#> Osage.County.Attorneys.Office                    1.65    2.0  0.59   2  33
#> Kansas.State.Highway.Patrol                      0.95    1.0  0.22   1  19
#> Kansas.State.Parks.and.Resources.Authority       1.25    1.0  0.55   2  25
#> Kansas.State.Game.and.Fish.Commission            1.25    1.0  0.55   2  25
#> Kansas.State.Department.of.Transportation        0.00    0.0    NA   0   0
#> US.Army.Corps.of.Engineers                       1.35    1.0  0.59   2  27
#> US.Army.Reserve                                  1.80    2.0  0.70   3  36
#> Crable.Ambulance                                 1.55    2.0  0.60   2  31
#> Franklin.County.Ambulance                        1.65    2.0  0.59   2  33
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.70    2.0  0.57   2  34
#> Shawnee.County.CD.Underwater.Rescue.Team         1.55    2.0  0.60   2  31
#> Burlingame.Police.Department                     1.80    2.0  0.52   2  36
#> Lyndon.Police.Department                         1.60    2.0  0.60   2  32
#> American.Red.Cross                               1.40    1.0  0.60   2  28
#> Topeka.Fire.Department.Rescue.No.1               2.30    2.5  0.86   3  46
#> Carbondale.Fire.Department                       0.00    0.0    NA   0   0
#> Topeka.Radiator.and.Body.Works                   1.70    2.0  0.57   2  34
v_distance(g_i, mode = "out")
#>                                               average median stdev max sum
#> Osage.County.Sheriffs.Department                 1.15    1.0  0.57   2  23
#> Osage.County.Civil.Defense.Office                1.25    1.0  0.61   2  25
#> Osage.Country.Coroners.Office                    1.40    2.0  0.70   3  28
#> Osage.County.Attorneys.Office                    1.30    1.5  0.62   2  26
#> Kansas.State.Highway.Patrol                      1.10    1.0  0.55   2  22
#> Kansas.State.Parks.and.Resources.Authority       1.25    1.0  0.61   2  25
#> Kansas.State.Game.and.Fish.Commission            1.35    1.5  0.71   3  27
#> Kansas.State.Department.of.Transportation        1.65    2.0  0.65   3  33
#> US.Army.Corps.of.Engineers                       1.40    2.0  0.70   3  28
#> US.Army.Reserve                                  1.65    2.0  0.62   3  33
#> Crable.Ambulance                                 1.40    2.0  0.70   3  28
#> Franklin.County.Ambulance                        1.55    2.0  0.67   3  31
#> Lees.Summit.Underwater.Rescue.Team..Missouri.    1.70    2.0  0.68   3  34
#> Shawnee.County.CD.Underwater.Rescue.Team         1.15    1.0  0.57   2  23
#> Burlingame.Police.Department                     0.90    1.0  0.34   2  18
#> Lyndon.Police.Department                         1.45    2.0  0.70   3  29
#> American.Red.Cross                               1.25    1.0  0.61   2  25
#> Topeka.Fire.Department.Rescue.No.1               1.55    2.0  0.67   3  31
#> Carbondale.Fire.Department                       1.50    2.0  0.69   3  30
#> Topeka.Radiator.and.Body.Works                   1.45    2.0  0.61   2  29