10^{11}$   $3.29\times 10^{7}$             3.35%
     30     Pharmaceutical products                                                                                                                   $1.4\times 10^{11}$    $2.59\times 10^{7}$             2.81%
     72     Iron and steel                                                                                                                            $1.35\times 10^{11}$   $2.77\times 10^{7}$             2.70%
     71     Pearls, precious stones, metals, coins, etc                                                                                               $1.01\times 10^{11}$   $2.41\times 10^{7}$             2.02%
     10     Cereals                                                                                                                                   $3.63\times 10^{10}$   $1.28\times 10^{7}$             0.73%
     52     Cotton, including yarn and woven fabric thereof                                                                                           $3.29\times 10^{10}$   $6.96\times 10^{6}$             0.66%
      9     Coffee, tea, mate & spices                                                                                                                $1.28\times 10^{10}$   $2.56\times 10^{6}$             0.26%
     93     Arms and ammunition, parts and accessories thereof                                                                                        $4.31\times 10^{9}$    $2.46\times 10^{6}$             0.09%
     ALL    Aggregate (all 97 commodities)                                                                                                            $4.99\times 10^{12}$   $3.54\times 10^{8}$            100.00%

The world trade multiplex \[sec:data1\]
---------------------------------------

As in refs. [@Squartini_etal_2011a_pre; @Squartini_etal_2011b_pre], we employ international trade data provided by the United Nations Commodity Trade Database (UN COMTRADE [^1]) in order to build a time sequence of binary and weighted trade multiplexes. The sample refers to $11$ years (1992-2002) and the money unit is current U.S. dollars. The choice of this time span allows us construct a time-varying multiplex with a constant number of $N=162$ countries and a constant number of $C=97$ layers (commodity classes), evolving over $T=11$ years. When necessary, we will also aggregate all or part of the layers to obtain different levels of resolution.

We chose the classification of trade values into 