A review of the maritime container shipping industry as a complex adaptive system words,low remoteness among the nodes).In the maritime setting this property has a significant value;the connections among ports can in fact create clusters of small specialized ports that gravitate around a large port(hub).The large port uses small sub-peripheral ports to sub-contract operations;by so doing,all the ports(hub and peripheral)reach their goals and increase the economic entropy of the system [28]. The expression of the clustering effect,Degree distributionP(k)shows that"most ports have few connections,but there are some ports linked to hundreds of other ports"[7].However, when the authors examine the degree distribution in detail,they find that the GCSN does not belong to the class of scale free networks.Both studies show low power law exponents or right skewed degree distributions,but if the authors had shown a ranking of the ports over time,the degree distribution analysis would have had a higher significance.This would have informed them if there had ever been a turnover of dominant hubs,which in turn had led to the detection of competitive markets in maritime shipping.Opposite results would have depicted a constrained market. Kaluza et al.[7]also studied the GCSN as a weighted network where the distribution of weights and Strength displays a power law regime with exponents higher than 1.This finding is in line with the existence of a few routes with high intensity traffic and a few ports that can handle large cargo traffic.The detection of power law regimes is often associated with inequality (i.e.distribution of income and wealth)or vulnerability in economic systems [28,29] The correlation between Strength and Degree of each node also fits a power law,implying that the amount of goods handled by each port grows faster than the number of connections with other ports.Hub ports also do not have a high number of connections with other ports, but the connected routes are used by a proportionally higher number of vessels. Ducruet and Notteboom's work [5]does not provide results of the weighted network analysis over years 1996 and 2006.An analysis of this type would have allowed us to discuss relevant facts about the dynamics of flows in the main interoceanic routes as well as give constructive criticism on the impacts of the introduction of large loading vessels (post-Panamax era)on specific routes. It is possible to inspect the centrality of ports in a network(i.e.the importance of a node)in addition to other topological measures.In the case of GCSN,both studies use measures of the Betweenness Centrality5.Kaluza et al.[7]emphasize a high correlation between Degree k and the Betweenness Centrality,thus validating the observation that hub ports are also central points of the network.Ducruet and Notteboom detect interesting anomalies in the centrality of certain ports.Large North American and Japanese ports are not in the top ranking positions in terms of network centrality despite their traffic volume.The most central ports in the network are the Suez and Panama Canals(as gateway passages),Shanghai(due to the large number of ships"visiting"the port)and ports like Antwerp(due to its high number of connections. Although maritime shipping has been experiencing a tremendous period of expansion in the last decade,the underlying network has a robust topological structure which has not changed in recent years.Kaluza et al.[7]observe the differences "in the movement patterns of different ship types."For example,container ships show regular movements between ports, which can be explained by the type of the service they provide;whereas dry carriers and oil tankers tend to move in a less regular manner because they change their routes according to the demand of goods they carry Finally,maritime shipping appears to have gained a stronger regional dimension over the years.In 1996 there was a stronger relation between European and Asian basins while in 2006 these connections appear to have weakened.Ducruet and Notteboom [5]explain this asA review of the maritime container shipping industry as a complex adaptive system 7 words, low remoteness among the nodes). In the maritime setting this property has a significant value; the connections among ports can in fact create clusters of small specialized ports that gravitate around a large port (hub). The large port uses small sub-peripheral ports to sub-contract operations; by so doing, all the ports (hub and peripheral) reach their goals and increase the economic entropy of the system [28]. The expression of the clustering effect, Degree distribution4 P(k) shows that “most ports have few connections, but there are some ports linked to hundreds of other ports” [7]. However, when the authors examine the degree distribution in detail, they find that the GCSN does not belong to the class of scale free networks. Both studies show low power law exponents or right skewed degree distributions, but if the authors had shown a ranking of the ports over time, the degree distribution analysis would have had a higher significance. This would have informed them if there had ever been a turnover of dominant hubs, which in turn had led to the detection of competitive markets in maritime shipping. Opposite results would have depicted a constrained market. Kaluza et al. [7] also studied the GCSN as a weighted network where the distribution of weights and Strength5 displays a power law regime with exponents higher than 1. This finding is in line with the existence of a few routes with high intensity traffic and a few ports that can handle large cargo traffic. The detection of power law regimes is often associated with inequality (i.e. distribution of income and wealth) or vulnerability in economic systems [28, 29]. The correlation between Strength and Degree of each node also fits a power law, implying that the amount of goods handled by each port grows faster than the number of connections with other ports. Hub ports also do not have a high number of connections with other ports, but the connected routes are used by a proportionally higher number of vessels. Ducruet and Notteboom’s work [5] does not provide results of the weighted network analysis over years 1996 and 2006. An analysis of this type would have allowed us to discuss relevant facts about the dynamics of flows in the main interoceanic routes as well as give constructive criticism on the impacts of the introduction of large loading vessels (post-Panamax era) on specific routes. It is possible to inspect the centrality of ports in a network (i.e. the importance of a node) in addition to other topological measures. In the case of GCSN, both studies use measures of the Betweenness Centrality6 . Kaluza et al. [7] emphasize a high correlation between Degree k and the Betweenness Centrality, thus validating the observation that hub ports are also central points of the network. Ducruet and Notteboom detect interesting anomalies in the centrality of certain ports. Large North American and Japanese ports are not in the top ranking positions in terms of network centrality despite their traffic volume. The most central ports in the network are the Suez and Panama Canals (as gateway passages), Shanghai (due to the large number of ships “visiting” the port) and ports like Antwerp (due to its high number of connections.) Although maritime shipping has been experiencing a tremendous period of expansion in the last decade, the underlying network has a robust topological structure which has not changed in recent years. Kaluza et al. [7] observe the differences “in the movement patterns of different ship types.” For example, container ships show regular movements between ports, which can be explained by the type of the service they provide; whereas dry carriers and oil tankers tend to move in a less regular manner because they change their routes according to the demand of goods they carry. Finally, maritime shipping appears to have gained a stronger regional dimension over the years. In 1996 there was a stronger relation between European and Asian basins while in 2006 these connections appear to have weakened. Ducruet and Notteboom [5] explain this as