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a growing literature has presented empirical findings of the persistent impact of trade activities on economic growth and poverty reduction ( portugal - perez,@xcite,@xcite,@xcite , ackah , @xcite ) . besides discussing on the relation between trade and development , they also report on the growth by destination hypothesis , according to which , the destination of exports can play an important role in determining the trade pattern of a country and its development path . simultaneously , there has been a growing interest in applying concepts and tools of network theory to the analysis of international trade ( serrano,@xcite,@xcite,@xcite , @xcite , picciolo,@xcite ) . trade networks are among the most cited examples of the use of network approaches . the international trade activity is an appealing example of a large - scale system whose underlying structure can be represented by a set of bilateral relations . this paper is a contribution to interweaving two lines of research that have progressed in separate ways : network analyses of international trade and the literature on african trade and development . the most intuitive way of defining a trade network is representing each world country by a vertex and the flow of imports / exports between them by a directed link . such descriptions of bilateral trade relations have been used in the gravity models ( @xcite ) where some structural and dynamical aspects of trade have been often accounted for .    while some authors have used network approaches to investigate the international trade activity , studies that apply network models to focus on specific issues of african trade are less prominent . although african countries are usually considered in international trade network analyses , the space they occupy in these literature is often very narrow .    this must be partly due to the existence of some relevant limitations that empirical data on african countries suffer from , mostly because part of african countries does not report trade data to the united nations . the usual solution in this case is to use partner country data , an approach referred to as * mirror statistics*. however , using mirror statistics is not a suitable source for bilateral trade in africa as an important part of intra - african trade concerns import and exports by non - reporting countries . a possible solution to overcome the limitations on bilateral trade data is to make use of information that , although concerning two specific trading countries , might be provided indirectly by a third and secondary source . that is what happens when we define a bipartite network and its one - mode projection . in so doing , each bilateral relation between two african countries in the network is defined from the relations each of these countries hold with another entity . it can be achieved in such a way that when they are similar enough in their relation with that other entity , a link is defined between them . our approach is applied to a subset of 49 african countries and based on the definition of two independent bipartite networks where trade similarities between each pair of african countries are used to define the existence of a link . in the first bipartite graph , the similarities concern a mutual leading destination of exports by each pair of countries and in the second bipartite graph , countries are linked through the existence of a mutual leading export commodity between them . therefore , bilateral trade discrepancies are avoided and we are able to look simultaneously at network structures that emerge from two fundamental characteristics ( exporting destinations and exporting commodities ) of  the international trade . as both networks were defined from empirical data reported for 2014 , we call these networks * destination share networks * *  * ( dsn@xmath0 ) and * *  * * * commodity share networks * ( csn@xmath1 , respectively . its worth noticing that the choice of a given network representation is only one out of several other ways to look at a given system . there may be many ways in which the elementary units and the links between them are conceived and the choices may depend strongly on the available empirical data and on the questions that a network analysis aims to address ( @xcite ) .    the main question addressed in this paper is whether some relevant characteristics of african trade would emerge from the bipartite networks above described . we hypothesized that specific characteristics could come out and shape the structures of both the dsn@xmath0 and the csn@xmath0 . we envision that these networks will allow to uncover some ordering emerging from african exports in the broader context of international trade . if it happens , the emerging patterns may help to understand important characteristics of african exports and its relation to other economic , geographic and organizational concerns .    to this end , the paper is organized as follows : next section presents the empirical data we work with , section three describes the methodology and some preliminary results from its application . in section four we present further results and discuss on their interpretation in the international trade setting . section five concludes and outlines future work . trade map - trade statistics for international business development ( itm ) - provides a dataset of import and export data in the form of tables , graphs and maps for a set of reporting and non - reporting countries all over the world . there are also indicators on export performance , international demand , alternative markets and competitive markets . trade map covers 220 countries and territories and 5300 products of the harmonized system ( hs code ) . since the trade map statistics capture nationally reported data of such a large amount of countries , this dataset is an appropriate source to the empirical study of temporal patterns emerging from international trade . nevertheless , some major limitations should be indicated , as for countries that do not report trade data to the united nations , trade map uses partner country data , an issue that motivated our choice for defining bipartite networks . our approach is applied to a subset of 49 african countries ( see table 1 ) and from this data source , trade similarities between each pair of countries are used to define networks of links between countries . table 1 shows the 49 african countries we have been working with . it also shows the regional organization of each country , accordingly to the following classification : 1 - southern african development community ( sadc ) ; 2 - unio do magreb rabe ( uma ) ; 3 - comunidade econmica dos estados da africa central ( ceeac ) ; 4 - common market for eastern and southern africa ( comesa ) and 5 - comunidade econmica dos estados da frica ocidental ( cedeao ) .    for each african country in table 1 , we consider the set of countries to which at least one of the african countries had exported in the year of 2014 . the specification of the destinations of exports of each country followed the international trade statistics database ( @xcite ) from where just * the first and the second main destinations of exports * * of each country * were taken .    similarly and also for each african country in table 1 , we took the set of commodities that at least one of the african countries had exported in 2014 . the specification of the destinations of exports of each country followed the same database from where just * the first and the second main export commodities of each country * were taken .    for each country ( column label `` * * country * * '' ) in table 1 , besides the regional organization ( column label `` * * o * * '' ) and the first and second destinations ( column labels `` * * destinations * * '' ) and commodities ( column labels `` * * products * * '' ) , we also considered the export value in 2014 ( as reported in @xcite ) so that the size of the representation of each country in the networks herein presented is proportional to its corresponding export value in 2014 .    [ cols="<,<,<,<,<,<,<,<,<,<,<,<,<,<",options="header " , ]     table 6 : comparing topological coefficients obtained for dsn@xmath0 and csn@xmath0 . the dsn@xmath0 also has a larger clustering coefficient than the csn@xmath0 , showing that when a country shares a mutual export destination with other two countries , these two other countries also tend to share a mutual export destination between them . the densities of dsn@xmath0 and csn@xmath0 confirm that topological distances in the dsn@xmath0 are shorter than in the csn@xmath2 and that on average going from one country in the dsn@xmath0 to any other country in the same graph takes less intermediate nodes than in the csn@xmath0 . although the networks dsn@xmath0 and csn@xmath0 inform about the degree of the nodes , their densely - connected nature does not help to discover any dominant topological pattern besides the distribution of the node s degree . moving away from a dense to a sparse representation of a network , one shall ensure that the degree of sparseness is determined endogenously , instead of by an a priory specification . it has been often accomplished ( araujo2012,@xcite ) through the construction of a minimal spanning tree ( mst ) , in so doing one is able to develop the corresponding representation of the network where sparseness replaces denseness in a suitable way .      in the construction of a mst by the _ nearest neighbor _ method , one defines the 49 countries ( in table 1 ) as the nodes ( @xmath3 ) of a weighted network where the distance @xmath4 between each pair of countries @xmath5 and @xmath6 corresponds to the inverse of weight of the link ( @xmath7 ) between @xmath5 and @xmath8 .    from the @xmath9 distance matrix @xmath10 , a hierarchical clustering is then performed using the _ nearest neighbor _ method . initially @xmath11 clusters corresponding to the @xmath11 countries are considered . then , at each step , two clusters @xmath12 and @xmath13 are clumped into a single cluster if    @xmath14    with the distance between clusters being defined by    @xmath15 with @xmath16 and @xmath17    this process is continued until there is a single cluster . this clustering process is also known as the _ single link method _ , being the method by which one obtains the minimal spanning tree ( mst ) of a graph .    in a connected graph , the mst is a tree of @xmath18 edges that minimizes the sum of the edge distances . in a network with @xmath11 nodes , the hierarchical clustering process takes @xmath18 steps to be completed , and uses , at each step , a particular distance @xmath19 @xmath20 @xmath10 to clump two clusters into a single one . in this section we discuss the results obtained from the mst of each one - mode projected graphs dsn@xmath0 and csn@xmath0 . as earlier mentioned , the mst of a graph may allow for discovering relevant topological patterns that are not easily observed in the dense original networks . as in the last section , we begin with the analysis of the dsn@xmath0 and then proceed to the csn@xmath0 . we look for eventual topological structures coming out from empirical data of african exports , in order to see whether some relevant characteristics of african trade have any bearing on the network structures that emerge from the application of our approach . in the last section , we observed some slight influence of the regional position of each country in its connectivity . with the construction of the minimum spanning trees we envision that some stronger structural patterns would come to be observed on the trees . figure 7 shows the mst obtained from the dsn@xmath0 and colored according to each country main destination of exports in 2014 . the first evidence coming out from the mst in figure 7 is the central position of ago clustering together the entire set of `` china '' exporters ( red ) in 2014 . another important pattern that emerges in the mst is the branch of uma countries ( yellow ) in the right side of the tree , being `` europe '' their most frequent destination of exports . similarly , part of the countries that export mostly to `` other '' seems to cluster on the left branch ( purple ) . interestingly , the countries that exports to `` african countries '' ( blue ) occupy the less central positions on the tree . this result illustrates the suitability of the mst to separate groups of african countries according to their main export destinations and the show how opposite are the situations of those that export to `` china '' from the countries that have africa itself as their main export destinations .    regarding centrality , ago occupies the most central position of the network since this country exports to the top most african export destinations ( `` china '' and `` europe '' ) being therefore , and by this means , easily connected to a large amount of other countries . indeed , ago is the center of the most central cluster of `` china '' exporters . on the other hand , many leaf positions are occupied by countries that exports to other african countries as they have the smallest centrality in the whole network , they are ken , nam and swa . their weak centrality is due to the fact that their leading export destinations are spread over several countries ( zmb , tza , zaf , bwa and ind ) . figure 8 shows the mst obtained from the csn@xmath0 and colored according to the main export commodity of each country in 2014 . the first observation on the mst presented in figure 8 is that , centrality is concentrated in a fewer number of countries ( when compared to the mst of the dsn@xmath0 ) . the top most central and connected positions are shared by countries belonging to two regional organizations : sadc and cedeao , being mainly represented by zaf and ago and clustering countries whose main export commodities are `` diamonds '' and `` petroleum '' , respectively . unsurprisingly , centrality and connectivity advantages seem to be concentrated in these two leading commodity partitions ( `` diamonds '' and `` petroleum '' ) and organization groups ( sadc and cedeao ) . indeed , half of cedeao countries  occupy the upper branch rooted in zaf and having `` diamonds''(yellow ) as their main export commodity . another regional cluster is rooted in ago and tie together several uma countries whose main export commodity is `` petroleum''(blue ) . on the other hand , half of uma countries are far from each other on the tree , they occupy the leaf positions , being weakly connected to the other african countries to which , the few connections they establish rely on having `` manufactured '' as their main export commodity . likewise , there is a branch clustering exporters of `` raw materials''(green ) being also placed at the leaf positions on the tree . such a lack of centrality of `` raw materials '' exporters in the csn@xmath0 seems to be due to the fact that their leading export products are spread over many different commodities ( the `` raw materials '' partition comprises 12 different commodities ) . in the last decade , a debate has taken place in the network literature about the application of network approaches to model international trade . in this context , and even though recent research suggests that african countries are among those to which exports can be a vehicle for poverty reduction , these countries have been insufficiently analyzed . we have proposed the definition of trade networks where each bilateral relation between two african countries is defined from the relations each of these countries hold with another entity . both networks were defined from empirical data reported for 2014.they are independent bipartite networks : a destination share network ( dsn@xmath0 ) and  a  commodity share network ( csn@xmath2 ) . in the former , two african countries are linked if they share a mutual leading destination of exports , and in the latter , countries are linked through the existence of a mutual leading export commodity between them . 1 .   sharing a mutual export destination happens more often : the very first remark coming out from the observation of both the dsn@xmath0 and  the csn@xmath2 is that , in 2014 and for the 49 african countries , sharing a mutual exporting product happens less often than sharing a mutual destination of exports . 2 .   great exporting countries tend to be more linked : there is a positive correlation between strong connected countries in both the dsn@xmath0 and the csn@xmath0 and those with high amounts of export values in 2014 . it is in line with recent research placed in two different branches of the literature on international trade : the world trade web ( wtw )  empirical exploration ( @xcite,@xcite,@xcite,@xcite , benedictis,@xcite,@xcite ) and the one that specifically focus on african trade ( @xcite,@xcite , morrissey,@xcite,@xcite,@xcite ) . references ( @xcite,@xcite ) reports on the role of export performance to economic growth . they also discuss on the relation between trade and development , and on the growth by destination hypothesis , according to which , the destination of exports can play an important role in determining the trade pattern of a country and its development path . destination matters : the idea that destination matters is in line with our finding that in the dsn@xmath0 , the highest connected nodes are those whose main export destination is china . according to baliamoune - lutz ( baliamoune ) export concentration enhances the growth effects of exporting to china , implying that countries which export one major commodity to china benefit more ( in terms of growth ) than do countries that have more diversified exports . * the china effect : one of the patterns that came out from our dsn@xmath0 shows that half of ceeac and sadc countries belongs to the bulk of `` china '' destination cluster , having high betweenness centrality . additionally , the `` china '' destination group of countries displays the highest clustering coefficient ( 0.96 ) , meaning that , besides having china as their main exports destination , the second destination of exports of the countries in this group is highly concentrated on a few countries . * the role of intra - african trade : another important pattern coming out from the mst of the dsn@xmath0 shows how opposite are the situations of the countries that export to `` china '' from the countries that have africa itself as their main export destinations . in the mst of the destination share network , many leaf positions are occupied by intra - african exporters as they have the smallest centrality in the whole network . their weak centrality is due to the fact that their leading export destinations are spread over several countries . this result is in line with the results reported by reference ( @xcite ) where the growing importance of intra - african trade is discussed and proven to be a crucial channel for the expansion of african exports . moreover , kamuganga found significant correlation between the participation in intra - african trade and the diversification of exports . * the angola cluster : our results highlighted the remarkable centrality of ago as the center of the most central cluster of `` china '' exporters . indeed , ago is the country that holds the most central position when both dsn@xmath0 and csn@xmath0 are considered . this country occupies in both cases the center of the largest central clusters : `` china '' exporters in dsn@xmath0 and exporters of `` petroleum '' in csn@xmath0 . * uma countries anti - diversification : in the opposite situation , we found that uma countries display very low centrality , showing that besides having `` europe '' as their first export destination , the second destination of exports of uma countries is spread over several countries . this result is in line with reference ( gamberoni ) report on european unilateral trade preferences and anti - diversification effects . we showed that uma countries occupy a separate branch in the mst of the dsn@xmath0 , being `` europe '' their most frequent destination of exports . 4 .   in the csn@xmath0 , the highest connected nodes are those that cluster as `` petroleum '' exporters , being followed by those that export `` diamonds '' . unsurprisingly , `` raw materials '' exporters display very low connectivity as their second main exporting product is spread over several different commodities . organizations matter : regional and organizational concerns seem to have some impact in the csn@xmath0 . * sadc and petroleum : the group of sadc countries , although comprising a large number of elements , is the one with the poorest connectivity and clustering in the csn@xmath0 . it is certainly due to the fact that without ago and moz , this large group of countries does not comprise `` petroleum '' exporters . * uma countries anti - diversification : again in the csn@xmath0 , its mst shows that uma countries are placed on a separate branch . although they are countries with high amounts of export values in 2014 , uma countries display low connectivity and low centrality . the leaf positions in the mst of either dsn@xmath0 or csn@xmath0 - while occupied by countries with very low centrality and connectivity - were shown to characterize countries that export mainly to `` europe '' and whose main exporting product is `` raw materials '' . future work is planned to be twofold . we plan to further improve the definition of networks of african countries , enlarging the set of similarities that define the links between countries in order to include aspects like mother language , currencies , demography and participation in trade agreements . on the other hand , we also plan to apply our approach to different time periods . as soon as we can relate the structural similarities            baliamoune - lutz , m. ( 2011 ) growth by destination ( where you export matters ) : trade with china and growth in african countries in african development review , special issue : special issue on the 2010 african economic conference on setting the agenda for africa s economic recovery and long - term growth  v.23 , 2 . gamberoni , e. ( 2007 ) do unilateral trade preferences help export diversification ? an investigation of the impact of the european unilateral trade preferences on intensive and extensive margin of trade , iheid working paper 17 . picciolo , f. ; squartini , t. ; ruzzenenti , f. ; basosi , r. and garlaschelli , d. ( 2012 ) the role of distances in the world trade web , _ in _ proceedings of the eighth international conference on signal - image technology & internet - based systems ( sitis 2012 ) .
this paper is a contribution to interweaving two lines of research that have progressed in separate ways : network analyses of international trade and the literature on african trade and development . 
 gathering empirical data on african countries has important limitations and so does the space occupied by african countries in the analyses of trade networks . here 
 , these limitations are dealt with by the definition of two independent bipartite networks : a destination share network and  a  commodity share network . 
 these networks - together with their corresponding minimal spanning trees - allow to uncover some ordering emerging from african exports in the broader context of international trade . 
 the emerging patterns help to understand important characteristics of african exports and its binding relations to other economic , geographic and organizational concerns as the recent literature on african trade , development and growth has shown . 
 financial support from national funds by fct ( fundao para a cincia e a tecnologia ) . 
 this article is part of the strategic project : uid / eco/00436/2013    keywords : trade networks , african exports , spanning trees , bipartite graphs