[1] FALSE[1] TRUE[1] TRUEThe original dataset is the trade dataset version 4 from the Correlates of War Project. In this subject I only use trade data of year 2014. The format is edgelist. The nodes are countries, and the ties are the trading relations between countries in 2014. The network is directed and weighted.
Let’s look at some basic descriptive facts.
[1] 186[1] 22451[1] 241.4086[1] 0.6524557 Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 0.2 4.9 813.1 78.5 472525.2 The network in 2014 has 186 nodes, i.e. 186 countries involed in the trading network. There are 22451 egdes. Each country has 241 connected edges on average. On average, one country import 813 million dollars of good from another country each year. 65% of potential ties exit.
Let’s classify all dyads and traids in the network:
$mut
[1] 9933
$asym
[1] 2585
$null
[1] 4687 [1] 75574 62420 92513 11021 9514 11233 60137 45246 5530
[10] 788 188669 14094 9006 15882 147383 306230[1] 0.07161783[1] 0.3484117[1] 1There are 9933 mutual bilateral trade relations, 2585 unilateral trade relations, and 4687 pairs of coutries have no trading in 2014.
7% of the triads are empty, and 65% of them are triangle. The network is quite dense.
Let’s look at the distribution of nodes centrality.
The betweenness centrality and reflected centrality are right-skewed as expected. Limited coutries locate at the central bridging positions. But the bonachic-power and closeness show nearly normal distribution, and the eigenvector centrality and deprived centrality are highly left-skewed, which might show a decentralized network. The deprived centrality contribute to largest part of eigenvector centrality. Every country kind of plays as bridge in the network.
name short_name ccode closeness betweenness
1 China CHN 710 0.9946237 316.8033
2 United States of America USA 2 0.9788360 306.0056
3 South Korea ROK 732 0.9840426 304.6967
4 Australia AUL 900 0.9788360 294.1315
5 India IND 750 0.9893048 224.8866
eigen bonpow rc eigen.rc dc eigen.dc
1 0.09697295 -1.2742959 0.18197333 0.017646489 0.8180267 0.07932646
2 0.09703836 -1.0694386 0.19337928 0.018765208 0.8066207 0.07827315
3 0.09650158 -1.1175464 0.07623579 0.007356874 0.9237642 0.08914470
4 0.09535200 -0.8813261 0.04057029 0.003868458 0.9594297 0.09148354
5 0.09695131 -1.1636487 0.02262084 0.002193121 0.9773792 0.09475819 name short_name ccode closeness betweenness
1 Bosnia and Herzegovina BOS 346 0.6271186 2.042703
2 Saudi Arabia SAU 670 0.8726415 83.004276
3 Zimbabwe ZIM 552 0.6902985 26.360045
4 Dominican Republic DOM 42 0.8258929 56.408439
5 Malaysia MAL 820 0.9487179 199.363801
eigen bonpow rc eigen.rc dc eigen.dc
1 0.05841333 0.6260723 0.0014683688 8.577231e-05 0.9985316 0.05832756
2 0.08701319 0.5455796 0.0317620070 2.763714e-03 0.9682380 0.08424948
3 0.07643869 0.5220134 0.0004566747 3.490762e-05 0.9995433 0.07640378
4 0.07625539 0.4349504 0.0020462397 1.560368e-04 0.9979538 0.07609935
5 0.09645108 0.4242820 0.0301872862 2.911596e-03 0.9698127 0.09353948 name short_name ccode closeness betweenness eigen
1 China CHN 710 0.9946237 316.8033 0.09697295
2 United Kingdom UKG 200 0.9893048 224.4011 0.09698944
3 France FRN 220 0.9893048 217.5927 0.09701673
4 India IND 750 0.9893048 224.8866 0.09695131
5 Netherlands NTH 210 0.9840426 213.0323 0.09701673
bonpow rc eigen.rc dc eigen.dc
1 -1.274296 0.18197333 0.017646489 0.8180267 0.07932646
2 -1.000833 0.03264763 0.003166476 0.9673524 0.09382296
3 -1.371359 0.04966972 0.004818793 0.9503303 0.09219793
4 -1.163649 0.02262084 0.002193121 0.9773792 0.09475819
5 -1.315812 0.10053860 0.009753925 0.8994614 0.08726280 name short_name ccode closeness betweenness
1 United States of America USA 2 0.9788360 306.0056
2 Netherlands NTH 210 0.9840426 213.0323
3 France FRN 220 0.9893048 217.5927
4 Denmark DEN 390 0.9536082 186.4211
5 Spain SPN 230 0.9536082 187.3443
eigen bonpow rc eigen.rc dc eigen.dc
1 0.09703836 -1.0694386 0.193379284 0.0187652082 0.8066207 0.07827315
2 0.09701673 -1.3158118 0.100538595 0.0097539253 0.8994614 0.08726280
3 0.09701673 -1.3713593 0.049669717 0.0048187933 0.9503303 0.09219793
4 0.09701673 -0.7065288 0.009933439 0.0009637097 0.9900666 0.09605302
5 0.09701673 -0.5716878 0.031197741 0.0030267026 0.9688023 0.09399002 name short_name ccode closeness betweenness eigen
1 Finland FIN 375 0.8809524 141.8394 0.09667270
2 Denmark DEN 390 0.9536082 186.4211 0.09701673
3 Turkey TUR 640 0.9840426 208.8803 0.09679966
4 Sweden SWD 380 0.9788360 205.9708 0.09677237
5 South Africa SAF 560 0.9736842 200.3019 0.09685164
bonpow rc eigen.rc dc eigen.dc
1 0.1099607 0.006087120 0.0005884583 0.9939129 0.09608424
2 -0.7065288 0.009933439 0.0009637097 0.9900666 0.09605302
3 -1.6759857 0.011984440 0.0011600897 0.9880156 0.09563957
4 -1.1753454 0.013861294 0.0013413903 0.9861387 0.09543098
5 -1.2631864 0.017108054 0.0016569431 0.9828919 0.09519470 name short_name ccode closeness betweenness
1 United States of America USA 2 0.9788360 306.0056
2 China CHN 710 0.9946237 316.8033
3 Canada CAN 20 0.9788360 192.3823
4 Netherlands NTH 210 0.9840426 213.0323
5 Germany GMY 255 0.9840426 219.4348
eigen bonpow rc eigen.rc dc eigen.dc
1 0.09703836 -1.0694386 0.1933793 0.018765208 0.8066207 0.07827315
2 0.09697295 -1.2742959 0.1819733 0.017646489 0.8180267 0.07932646
3 0.09672468 -1.3044404 0.1339702 0.012958227 0.8660298 0.08376646
4 0.09701673 -1.3158118 0.1005386 0.009753925 0.8994614 0.08726280
5 0.09689933 -0.6890218 0.1000104 0.009690938 0.8999896 0.08720839Among these countries, China has the highest betweenness and closeness centrality. Bosnia and Herzegovina has the highest bonachic-power. The US gets the highest deprived centrality and eigenvector centrality. The Finland has the highest reflected centrality. On the top 5 lits of betweenness, closeness, deprived and eigenvector centrality, all we see are familiar trading powerhouses. But the list of bonachic-power has many unexpected country on the list. I don’t really understand what bonachic-power presents so don’t know how to interpret this outcome.
term closeness eigen betweenness eigen.dc eigen.rc bonpow
1 closeness .91 .91 .89 .51 -.02
2 eigen .91 .79 .99 .41 .01
3 betweenness .91 .79 .74 .66 -.04
4 eigen.dc .89 .99 .74 .31 .02
5 eigen.rc .51 .41 .66 .31 -.07
6 bonpow -.02 .01 -.04 .02 -.07 
Closeness, betweeness, eigenvector centrality are highly corralled. They are interchangeable measures of centrality. The reflected centrality is less correlated with those parameters, but it’s a small part compared with the deprived one. Bonachic-power is almost independent with other parameters.
