Import Data
nodes=read.csv("nodes_airlines.csv")
edges=read.csv("edges_airlines.csv")
library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
library(ggraph)
## Loading required package: ggplot2
library(tidygraph)
##
## Attaching package: 'tidygraph'
## The following object is masked from 'package:igraph':
##
## groups
## The following object is masked from 'package:stats':
##
## filter
library(visNetwork)
library(maps)
Plotting Edges Network
g1=graph_from_data_frame(edges)
plot(g1)
network1=graph_from_data_frame(d=edges,vertices=nodes,directed=T)
plot(network1)
Mapping United States and adding Airport Nodes
map("world", regions=c("usa"), fill=T, col="white", bg="white", ylim=c(21.0,50.0), xlim=c(-130.0,-65.0))
points(nodes$longitude,nodes$latitude, pch=3, cex=0.1, col="red")
Interactive Graph
node_labels <- nodes$Code
vis_net <- toVisNetworkData(network1)
visNetwork(vis_net$nodes, vis_net$edges) %>%
visEdges(arrows = "to") %>%
visNodes(label = node_labels)%>%
visLayout(randomSeed = 456)
Circular Layout
tidy_net <- as_tbl_graph(network1)
ggraph(tidy_net, layout = "circle") +
geom_edge_link() +
geom_node_point() +
geom_node_text(aes(label = Code), vjust = 1.5) +
theme_void()
## 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.
Centrality Measures
degree_centrality_net <- degree(network1, mode = "all", normalized = TRUE)
print(degree_centrality_net)
## 0 1 2 3 4 5
## 0.042735043 0.021367521 0.042735043 0.008547009 0.034188034 0.012820513
## 6 7 8 9 10 11
## 0.008547009 0.012820513 0.025641026 0.076923077 0.072649573 0.076923077
## 12 13 14 15 16 17
## 0.017094017 0.021367521 0.025641026 0.004273504 0.004273504 0.008547009
## 18 19 20 21 22 23
## 0.055555556 0.047008547 0.051282051 0.034188034 0.055555556 0.008547009
## 24 25 26 27 28 29
## 0.055555556 0.055555556 0.012820513 0.042735043 0.025641026 0.004273504
## 30 31 32 33 34 35
## 0.047008547 0.025641026 0.012820513 0.008547009 0.034188034 0.076923077
## 36 37 38 39 40 41
## 0.038461538 0.094017094 0.012820513 0.008547009 0.008547009 0.324786325
## 42 43 44 45 46 47
## 0.004273504 0.025641026 0.089743590 0.094017094 0.008547009 0.004273504
## 48 49 50 51 52 53
## 0.085470085 0.081196581 0.534188034 0.012820513 0.004273504 0.055555556
## 54 55 56 57 58 59
## 0.008547009 0.025641026 0.012820513 0.008547009 0.004273504 0.064102564
## 60 61 62 63 64 65
## 0.008547009 0.051282051 0.008547009 0.025641026 0.008547009 0.055555556
## 66 67 68 69 70 71
## 0.012820513 0.004273504 0.038461538 0.094017094 0.346153846 0.021367521
## 72 73 74 75 76 77
## 0.029914530 0.004273504 0.025641026 0.055555556 0.008547009 0.064102564
## 78 79 80 81 82 83
## 0.025641026 0.029914530 0.478632479 0.008547009 0.068376068 0.025641026
## 84 85 86 87 88 89
## 0.260683761 0.012820513 0.051282051 0.158119658 0.025641026 0.004273504
## 90 91 92 93 94 95
## 0.029914530 0.012820513 0.025641026 0.042735043 0.004273504 0.034188034
## 96 97 98 99 100 101
## 0.055555556 0.055555556 0.047008547 0.012820513 0.004273504 0.029914530
## 102 103 104 105 106 107
## 0.059829060 0.042735043 0.042735043 0.102564103 0.111111111 0.004273504
## 108 109 110 111 112 113
## 0.004273504 0.102564103 0.017094017 0.085470085 0.017094017 0.008547009
## 114 115 116 117 118 119
## 0.017094017 0.072649573 0.004273504 0.008547009 0.128205128 0.055555556
## 120 121 122 123 124 125
## 0.076923077 0.004273504 0.025641026 0.034188034 0.008547009 0.038461538
## 126 127 128 129 130 131
## 0.004273504 0.081196581 0.081196581 0.004273504 0.384615385 0.034188034
## 132 133 134 135 136 137
## 0.064102564 0.021367521 0.068376068 0.042735043 0.555555556 0.017094017
## 138 139 140 141 142 143
## 0.021367521 0.004273504 0.008547009 0.017094017 0.004273504 0.004273504
## 144 145 146 147 148 149
## 0.004273504 0.025641026 0.021367521 0.008547009 0.012820513 0.021367521
## 150 151 152 153 154 155
## 0.012820513 0.008547009 0.085470085 0.004273504 0.316239316 0.004273504
## 156 157 158 159 160 161
## 0.008547009 0.047008547 0.004273504 0.042735043 0.021367521 0.008547009
## 162 163 164 165 166 167
## 0.021367521 0.072649573 0.042735043 0.034188034 0.004273504 0.004273504
## 168 169 170 171 172 173
## 0.094017094 0.051282051 0.008547009 0.076923077 0.098290598 0.153846154
## 174 175 176 177 178 179
## 0.008547009 0.034188034 0.076923077 0.012820513 0.008547009 0.025641026
## 180 181 182 183 184 185
## 0.034188034 0.029914530 0.029914530 0.025641026 0.004273504 0.059829060
## 186 187 188 189 190 191
## 0.017094017 0.008547009 0.012820513 0.106837607 0.029914530 0.042735043
## 192 193 194 195 196 197
## 0.213675214 0.047008547 0.064102564 0.076923077 0.004273504 0.021367521
## 198 199 200 201 202 203
## 0.034188034 0.012820513 0.235042735 0.008547009 0.012820513 0.076923077
## 204 205 206 207 208 209
## 0.034188034 0.059829060 0.029914530 0.042735043 0.017094017 0.004273504
## 210 211 212 213 214 215
## 0.038461538 0.038461538 0.029914530 0.059829060 0.008547009 0.021367521
## 216 217 218 219 220 221
## 0.012820513 0.025641026 0.029914530 0.051282051 0.004273504 0.021367521
## 222 223 224 225 226 227
## 0.004273504 0.059829060 0.017094017 0.008547009 0.021367521 0.008547009
## 228 229 230 231 232 233
## 0.012820513 0.051282051 0.008547009 0.004273504 0.004273504 0.008547009
## 234
## 0.008547009
closeness_centrality_net <- closeness(network1, mode = "all", normalized = TRUE)
print(closeness_centrality_net)
## 0 1 2 3 4 5 6 7
## 0.4756098 0.4382022 0.4689379 0.4006849 0.4543689 0.4134276 0.4000000 0.3567073
## 8 9 10 11 12 13 14 15
## 0.4642857 0.4905660 0.4864865 0.4915966 0.4048443 0.3836066 0.4216216 0.3622291
## 16 17 18 19 20 21 22 23
## 0.4098074 0.3644860 0.4804928 0.4552529 0.4698795 0.4246824 0.4552529 0.3667712
## 24 25 26 27 28 29 30 31
## 0.4448669 0.4785276 0.4112478 0.4689379 0.4148936 0.3972835 0.4534884 0.4440228
## 32 33 34 35 36 37 38 39
## 0.4006849 0.4390244 0.4736842 0.4834711 0.4482759 0.4989339 0.4027539 0.4105263
## 40 41 42 43 44 45 46 47
## 0.4105263 0.5835411 0.3250000 0.4500000 0.4957627 0.4895397 0.3817292 0.3250000
## 48 49 50 51 52 53 54 55
## 0.5120350 0.4885177 0.6628895 0.4112478 0.3972835 0.4597250 0.4112478 0.4223827
## 56 57 58 59 60 61 62 63
## 0.4193548 0.4000000 0.3993174 0.4775510 0.3667712 0.4431818 0.4105263 0.4309392
## 64 65 66 67 68 69 70 71
## 0.4105263 0.4947146 0.3768116 0.3972835 0.4633663 0.5142857 0.5939086 0.4317343
## 72 73 74 75 76 77 78 79
## 0.4440228 0.3993174 0.4457143 0.4717742 0.3842365 0.4615385 0.4277879 0.4727273
## 80 81 82 83 84 85 86 87
## 0.6573034 0.3817292 0.4606299 0.4333333 0.5611511 0.4186047 0.4579256 0.5154185
## 88 89 90 91 92 93 94 95
## 0.4069565 0.3250000 0.4341373 0.3986371 0.4534884 0.4465649 0.3250000 0.4423440
## 96 97 98 99 100 101 102 103
## 0.4561404 0.4746450 0.4698795 0.4112478 0.3732057 0.4570312 0.4844720 0.4606299
## 104 105 106 107 108 109 110 111
## 0.4500000 0.4936709 0.4968153 0.3972835 0.3357245 0.5154185 0.4193548 0.4885177
## 112 113 114 115 116 117 118 119
## 0.4034483 0.3817292 0.4423440 0.4915966 0.3622291 0.3979592 0.5258427 0.4824742
## 120 121 122 123 124 125 126 127
## 0.4834711 0.3732057 0.4231465 0.4423440 0.4105263 0.4680000 0.3732057 0.5131579
## 128 129 130 131 132 133 134 135
## 0.4885177 0.3972835 0.6141732 0.4491363 0.4915966 0.4431818 0.4775510 0.4736842
## 136 137 138 139 140 141 142 143
## 0.6923077 0.4309392 0.4178571 0.3451327 0.4105263 0.4141593 0.3732057 0.3250000
## 144 145 146 147 148 149 150 151
## 0.3250000 0.4027539 0.4301471 0.4105263 0.3768116 0.4020619 0.4027539 0.4390244
## 152 153 154 155 156 157 158 159
## 0.4926316 0.4098074 0.5835411 0.3972835 0.3639191 0.4736842 0.3357245 0.4698795
## 160 161 162 163 164 165 166 167
## 0.4365672 0.4105263 0.4365672 0.4947146 0.4689379 0.4457143 0.3622291 0.3357245
## 168 169 170 171 172 173 174 175
## 0.4947146 0.4814815 0.4105263 0.4875000 0.4957627 0.5043103 0.3639191 0.4597250
## 176 177 178 179 180 181 182 183
## 0.5043103 0.3567073 0.4027539 0.3972835 0.4309392 0.4526112 0.4406780 0.4398496
## 184 185 186 187 188 189 190 191
## 0.3250000 0.4795082 0.4341373 0.3667712 0.3714286 0.5200000 0.4317343 0.4936709
## 192 193 194 195 196 197 198 199
## 0.5518868 0.4968153 0.4885177 0.5053996 0.3451327 0.4357542 0.4775510 0.4398496
## 200 201 202 203 204 205 206 207
## 0.5665860 0.3817292 0.4112478 0.5098039 0.4661355 0.4936709 0.4333333 0.4689379
## 208 209 210 211 212 213 214 215
## 0.4055459 0.3622291 0.4680000 0.4270073 0.4262295 0.4834711 0.4055459 0.4020619
## 216 217 218 219 220 221 222 223
## 0.3762058 0.4293578 0.4543689 0.4814815 0.3993174 0.4083770 0.3622291 0.4864865
## 224 225 226 227 228 229 230 231
## 0.4134276 0.4105263 0.4440228 0.4006849 0.4223827 0.4814815 0.4027539 0.3972835
## 232 233 234
## 0.3622291 0.4119718 0.4000000
betweenness_centrality_net <- betweenness(network1, directed = TRUE, normalized = TRUE)
print(betweenness_centrality_net)
## 0 1 2 3 4 5
## 2.372518e-03 7.636180e-04 1.584588e-03 4.929799e-04 9.375953e-04 2.541930e-03
## 6 7 8 9 10 11
## 0.000000e+00 4.464886e-03 1.706959e-03 5.647612e-03 6.506187e-03 9.056403e-03
## 12 13 14 15 16 17
## 1.494465e-02 3.788040e-03 3.415714e-03 0.000000e+00 0.000000e+00 0.000000e+00
## 18 19 20 21 22 23
## 2.510214e-02 4.486087e-03 2.772675e-03 2.686726e-03 1.420158e-02 0.000000e+00
## 24 25 26 27 28 29
## 1.399180e-02 8.925483e-04 0.000000e+00 1.216150e-02 1.014753e-03 0.000000e+00
## 30 31 32 33 34 35
## 5.358465e-03 1.058450e-03 0.000000e+00 0.000000e+00 8.145941e-04 4.608668e-03
## 36 37 38 39 40 41
## 3.266723e-03 1.559798e-02 7.747927e-04 0.000000e+00 0.000000e+00 4.405968e-02
## 42 43 44 45 46 47
## 0.000000e+00 1.821386e-03 1.230221e-02 1.993564e-02 0.000000e+00 0.000000e+00
## 48 49 50 51 52 53
## 5.352091e-03 1.564625e-02 1.122768e-01 3.668244e-05 0.000000e+00 5.885745e-03
## 54 55 56 57 58 59
## 0.000000e+00 1.911561e-04 2.248733e-03 0.000000e+00 0.000000e+00 5.740056e-03
## 60 61 62 63 64 65
## 0.000000e+00 7.392187e-03 0.000000e+00 3.616889e-03 0.000000e+00 5.608091e-03
## 66 67 68 69 70 71
## 0.000000e+00 0.000000e+00 3.033808e-03 1.388372e-02 5.370723e-02 5.706186e-04
## 72 73 74 75 76 77
## 1.580691e-04 0.000000e+00 1.865079e-03 2.801463e-03 0.000000e+00 5.557765e-03
## 78 79 80 81 82 83
## 7.527634e-04 1.437509e-04 1.062795e-01 0.000000e+00 5.113746e-03 5.180160e-04
## 84 85 86 87 88 89
## 4.773755e-02 1.867389e-04 9.915413e-04 1.669120e-02 1.436729e-04 0.000000e+00
## 90 91 92 93 94 95
## 1.319484e-04 0.000000e+00 6.624054e-04 1.693794e-03 0.000000e+00 7.521704e-05
## 96 97 98 99 100 101
## 5.208285e-03 1.982989e-03 2.010302e-03 9.170610e-06 0.000000e+00 9.318198e-04
## 102 103 104 105 106 107
## 2.428867e-03 4.784379e-04 6.809222e-03 9.209185e-03 1.164343e-02 0.000000e+00
## 108 109 110 111 112 113
## 0.000000e+00 1.936824e-02 0.000000e+00 9.269581e-03 2.997757e-03 0.000000e+00
## 114 115 116 117 118 119
## 9.523229e-05 1.522196e-02 0.000000e+00 0.000000e+00 2.479915e-02 2.751874e-03
## 120 121 122 123 124 125
## 6.982198e-03 0.000000e+00 3.313075e-03 3.409336e-03 0.000000e+00 8.885425e-04
## 126 127 128 129 130 131
## 0.000000e+00 2.291268e-03 6.338585e-03 0.000000e+00 1.048652e-01 3.975896e-03
## 132 133 134 135 136 137
## 7.954682e-03 2.020435e-03 8.381849e-03 2.255385e-04 1.468414e-01 2.215415e-04
## 138 139 140 141 142 143
## 4.728212e-04 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## 144 145 146 147 148 149
## 0.000000e+00 1.575904e-03 1.491980e-03 0.000000e+00 0.000000e+00 1.146157e-03
## 150 151 152 153 154 155
## 0.000000e+00 0.000000e+00 9.414358e-03 0.000000e+00 3.141194e-02 0.000000e+00
## 156 157 158 159 160 161
## 0.000000e+00 3.221191e-03 0.000000e+00 1.387858e-03 0.000000e+00 0.000000e+00
## 162 163 164 165 166 167
## 0.000000e+00 0.000000e+00 8.961235e-03 0.000000e+00 0.000000e+00 0.000000e+00
## 168 169 170 171 172 173
## 1.295569e-02 2.826099e-03 0.000000e+00 3.212118e-03 1.971671e-02 2.850949e-02
## 174 175 176 177 178 179
## 0.000000e+00 1.587206e-03 1.020936e-02 1.094553e-04 0.000000e+00 8.428648e-03
## 180 181 182 183 184 185
## 5.223013e-03 3.135598e-03 2.090438e-03 0.000000e+00 0.000000e+00 4.755451e-03
## 186 187 188 189 190 191
## 2.311918e-05 0.000000e+00 0.000000e+00 4.793957e-03 1.075538e-03 5.089891e-03
## 192 193 194 195 196 197
## 3.215780e-02 3.067820e-03 2.259175e-03 1.469593e-03 0.000000e+00 3.068035e-04
## 198 199 200 201 202 203
## 2.804434e-03 0.000000e+00 5.659473e-02 0.000000e+00 0.000000e+00 2.212609e-02
## 204 205 206 207 208 209
## 1.465119e-02 6.458850e-03 2.748767e-03 8.839685e-04 4.590094e-04 0.000000e+00
## 210 211 212 213 214 215
## 1.848105e-03 1.528875e-03 3.994359e-04 2.975317e-03 3.402056e-05 1.146169e-04
## 216 217 218 219 220 221
## 0.000000e+00 4.127253e-03 0.000000e+00 3.862067e-03 0.000000e+00 4.507494e-05
## 222 223 224 225 226 227
## 0.000000e+00 8.786504e-03 6.581045e-04 0.000000e+00 1.664846e-03 0.000000e+00
## 228 229 230 231 232 233
## 0.000000e+00 4.820933e-03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## 234
## 0.000000e+00
pagerank_centrality_net <- page_rank(network1)$vector
print(pagerank_centrality_net)
## 0 1 2 3 4 5
## 0.0061074752 0.0036528199 0.0071523919 0.0023392159 0.0048561105 0.0017803882
## 6 7 8 9 10 11
## 0.0033333827 0.0014295547 0.0042401911 0.0109927363 0.0067398683 0.0165033089
## 12 13 14 15 16 17
## 0.0030190672 0.0033734551 0.0023441386 0.0008823592 0.0009974990 0.0019787700
## 18 19 20 21 22 23
## 0.0077618008 0.0082233631 0.0060649941 0.0044354834 0.0090825680 0.0011104706
## 24 25 26 27 28 29
## 0.0094237466 0.0091605461 0.0032603403 0.0054438086 0.0049631026 0.0013980807
## 30 31 32 33 34 35
## 0.0059465589 0.0047391165 0.0027574212 0.0025484760 0.0042404735 0.0129657687
## 36 37 38 39 40 41
## 0.0066610017 0.0115507476 0.0023392159 0.0013325006 0.0018453732 0.0348829939
## 42 43 44 45 46 47
## 0.0007882390 0.0044260434 0.0139672023 0.0127824277 0.0024805930 0.0007882390
## 48 49 50 51 52 53
## 0.0104230697 0.0088220073 0.0547403617 0.0013296656 0.0013980807 0.0058232389
## 54 55 56 57 58 59
## 0.0014847577 0.0021355628 0.0026646314 0.0011955646 0.0007882390 0.0057080253
## 60 61 62 63 64 65
## 0.0011104706 0.0059648119 0.0009974990 0.0057445377 0.0018453732 0.0056853809
## 66 67 68 69 70 71
## 0.0016667258 0.0013980807 0.0051595804 0.0161040726 0.0312912537 0.0036090945
## 72 73 74 75 76 77
## 0.0015631367 0.0007882390 0.0031056063 0.0035559265 0.0011705465 0.0105957631
## 78 79 80 81 82 83
## 0.0065146082 0.0039430334 0.0315682747 0.0011705465 0.0061003820 0.0033037585
## 84 85 86 87 88 89
## 0.0225620268 0.0017012614 0.0033030079 0.0111023321 0.0011955646 0.0007882390
## 90 91 92 93 94 95
## 0.0018244815 0.0023062404 0.0031587769 0.0033117030 0.0007882390 0.0023055196
## 96 97 98 99 100 101
## 0.0027109882 0.0034420005 0.0026738070 0.0013325006 0.0007882390 0.0026070734
## 102 103 104 105 106 107
## 0.0025488139 0.0050263976 0.0056007428 0.0079325412 0.0162828314 0.0007882390
## 108 109 110 111 112 113
## 0.0010163504 0.0092361336 0.0011705465 0.0083015072 0.0042831683 0.0015055481
## 114 115 116 117 118 119
## 0.0012956525 0.0051592091 0.0008823592 0.0007882390 0.0075448908 0.0045298842
## 120 121 122 123 124 125
## 0.0075333840 0.0007882390 0.0027202299 0.0054347776 0.0009974990 0.0056351887
## 126 127 128 129 130 131
## 0.0007882390 0.0028759418 0.0066323583 0.0007882390 0.0233882220 0.0047953922
## 132 133 134 135 136 137
## 0.0063283015 0.0030824624 0.0060442153 0.0023684403 0.0199412508 0.0010254101
## 138 139 140 141 142 143
## 0.0025481456 0.0007882390 0.0007882390 0.0007882390 0.0007882390 0.0007882390
## 144 145 146 147 148 149
## 0.0007882390 0.0030927856 0.0019358365 0.0010115734 0.0010254101 0.0013682440
## 150 151 152 153 154 155
## 0.0019804385 0.0007882390 0.0107634243 0.0007882390 0.0142578840 0.0007882390
## 156 157 158 159 160 161
## 0.0008823592 0.0048189974 0.0010163504 0.0019843854 0.0012535215 0.0007882390
## 162 163 164 165 166 167
## 0.0012535215 0.0007882390 0.0030532863 0.0007882390 0.0008823592 0.0010163504
## 168 169 170 171 172 173
## 0.0083342141 0.0028779725 0.0011232406 0.0032512617 0.0062677886 0.0056356934
## 174 175 176 177 178 179
## 0.0008823592 0.0025271617 0.0039789989 0.0013957998 0.0007882390 0.0044227847
## 180 181 182 183 184 185
## 0.0037923738 0.0033574568 0.0030587121 0.0010254101 0.0007882390 0.0035076882
## 186 187 188 189 190 191
## 0.0011955646 0.0008823592 0.0008823592 0.0021928386 0.0015553479 0.0050863663
## 192 193 194 195 196 197
## 0.0062269209 0.0029364878 0.0017128919 0.0014667005 0.0007882390 0.0020155059
## 198 199 200 201 202 203
## 0.0020547897 0.0007882390 0.0049828332 0.0007882390 0.0007882390 0.0045630888
## 204 205 206 207 208 209
## 0.0033544461 0.0037439020 0.0019963824 0.0017071641 0.0011434137 0.0007882390
## 210 211 212 213 214 215
## 0.0013851682 0.0026048471 0.0016762039 0.0040180647 0.0022001697 0.0015003198
## 216 217 218 219 220 221
## 0.0007882390 0.0032414107 0.0007882390 0.0035937791 0.0007882390 0.0010115734
## 222 223 224 225 226 227
## 0.0007882390 0.0039707086 0.0016613679 0.0007882390 0.0028188888 0.0007882390
## 228 229 230 231 232 233
## 0.0007882390 0.0034829413 0.0013780504 0.0007882390 0.0007882390 0.0007882390
## 234
## 0.0007882390