This study uses the tools of network analysis to graphically and analytically represent the characteristics of world trade. The structure of the World Trade Network is compared over time, detecting and interpreting patterns of trade ties among countries. The results show that the trading system has become more intensely interconnected, and trade integration at the world level has been increasing, but it is still far from being complete, with the exception of a few areas. At the same time, we observed a strong and increasing heterogeneity in countries’ choice of trade partners, with countries holding very different positions within the network, so that it is very difficult to talk of a representative country in the international trade system. Network indices were also used in a gravity model regression, adding to the explanatory power of the model. Furthermore, the analysis shows that trade policies do play a role in shaping the trade network, and that WTO members are more closely connected than the rest of the world. The structural difference between the extensive and the intensive margin of trade is also highlighted. An important feature of these results is that they pertain to the trading system as a whole, giving a unified view of the system characteristics and complexity.

With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed and/or weighted networks too, thus properly exploiting richer information. In this work, we contribute to the effort of comparing the different methods for comparing networks and providing a guide for the selection of an appropriate one. First, we review and classify a collection of network comparison methods, highlighting the criteria they are based on and their advantages and drawbacks. The set includes methods requiring known node-correspondence, such as DeltaCon and Cut Distance, as well as methods not requiring a priori known node-correspondence, such as alignment-based, graphlet-based, and spectral methods, and the recently proposed Portrait Divergence and NetLSD. We test the above methods on synthetic networks and we assess their usability and the meaningfulness of the results they provide. Finally, we apply the methods to two real-world datasets, the European Air Transportation Network and the FAO Trade Network, in order to discuss the results that can be drawn from this type of analysis.

HighlightsThis paper explores the World Trade using the Network Analysis and introduces the reader to some of the techniques used to visualize, calculate and synthetically represent network trade data. The paper shows different visualizations of the network and describe its topological properties, producing and discussing some of the commonly used Network's statistics, and presenting some specific topics. All in all, this paper shows that Network Analysis is a useful tool to describe bilateral trade relations among countries when interdependence matters, and when trade relations are characterized by high dimensionality and strong heterogeneity. AbstractIn this paper we explore the BACI-CEPII database using Network Analysis. Starting from the visualization of the World Trade Network, we then define and describe the topology of the network, both in its binary version and in its weighted version, calculating and discussing some of the commonly used network's statistics. We finally discuss some specific topics that can be studied using Network Analysis and International Trade data, both at the aggregated and sectoral level. The analysis is done using multiple software (Stata, R, and Pajek). The scripts to replicate part of the analysis are included in the appendix, and can be used as an handson tutorial. Moreover,the World Trade Network local and global centrality measures, for the unweighted and the weighted version of the Network, calculated using the bilateral aggregate trade data for each country (178 in total) and each year (from 1995 to 2010,) can be downloaded from the CEPII webpage.JEL Classification: F10

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F14, F15,