There is a relationship between the trade flows of the countries and their economic growth potential and development. One way to analyze the trade flows of countries with each other is the use of network analysis techniques. Network analysis uses a visual, mathematical and statistical approach to identify and quantify the structural properties of networks. In this study, the relationship of the first 50 countries in the world in terms of export volume was evaluated with Gephi 0.9.2, one of the social network analysis programs. The effect of the Covid-19 epidemic disease on the commercial flows of the countries is the main purpose of the study. The existence of the effective role of geographical proximity in commercial flows, the commercial partners of the countries, the centrality criteria and the existence of countries that have an active role in the world are clarified with this study.
Research Article
Network analysis of International Export Pattern
https://doi.org/10.21203/rs.3.rs-1538734/v1
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Gephi
Social Network Analysis
Trade Flow
Export
Every country on the planet must have ties with one another. These forms of partnerships might be commercial, political, economic, and so on. Each sort of interaction brings different values to countries. Releationship levels are critical for a country's growth and improvement in wellbeing. The amount of trade ties is directly proportional to the countries’ level of development and the rate of economic growth. With international commerce, countries' development accelerates, their productivity rises, and their growth accelerates at the same rate. Over time the change in global production and consumption has resulted in the establishment of connections between different countries, resulting in the formation of a complex (Wei and Liu 2012). Hilgerdt (1943) and Deguchi et al. (2014) stated that trade networks were created following the Second World War. Since then, global trade networks have been and continue to be studied in various fields.
We can observe that more than one approach is used in the study of relations. Traditional techniques of assessing trade connection take into consideration countries' export-import values and the percentage of exports in GDP. These techniques only reflect the characteristics of the countries. The connection between countries can be omitted. At this stage, the Social Network Analysis (SNA) approach allows us to analyze country’s based on their position and structure in the network by concentrating on the relationships. Network analysis is a method that may be applied in a variety of fields, has its own methodologies and explanations, and requires specialized software. We can see that SNA has been employed extensively recently in the examination of global commerce networks. The representation of trade flow networks displays the relationship between countries in the network as well as the structure or systematic element of the network (Benedictis and Tajoli 2011).
There are three primary methodologies in network analysis: graph theory, statistics and probability, and algebraic theories (Wasserman and Faust 1994: 15). Graph theory, is an applied field of mathematics and is the most developed branch of recent years. It is employed efficiently in computer science, particularly in the solution of complicated problems. A network, in its most basic form, comprises of nodes (node, vertex) and visuals that provide information on the connections (edge) between nodes. A graph is the depiction of nodes and ties in a network. Nodes can symbolize a person, organization, nation, or website, among other things. Each node has its own set of connections. Non-directional (symmetrical) connections are indicated with a single straight line, whereas directional (asymmetrical) connections are shown with two straight lines. We used directed and weighted connections in our research. Each country is represented by a node, and the amount of cross-country exports is represented by directed connections.
The most crucial aspect that separates SNA from other approaches is that it examines and tests ideas using structural or relational information (Wasserman and Faust 1994). Unlike traditional research, SNA focuses on interactions between things rather than relationships between variables (dependent/independent).
The structural attributes of nodes, groups, or the complete network may be calculated and mapped using SNA. SNA can produce basic, descriptive statistical data that characterizes network interactions between nodes. The main difference between SNA and statistics is that SNA is concerned with the qualities of edges between nodes. SNA tools use several techniques to depict ensembles or clusters, ranks, and locations while also analyzing the studies with an analytical methodology, allowing node statistics and edge filtering. Centrality statistics offer information on the responsibilities of nodes in the network, and the interpretation of each centrality number and its relevance within the network varies. A node with a good value in one centrality measure cannot be expected to rank in the same order in other centrality measures. Similarly, it is not appropriate for the network analysis to clearly state that a node will be in a very good position in a single centrality criterion and that other centrality criteria will be the same.
Basic analytic methods are explored in the literature within the context of displaying social network data or constructing graphs, as well as performing other numerical analyses on the data. Figures (or graphs) are used to visualize data in various areas. When it comes to explaining what is desired, a well-structured graphical depiction outperforms numerous The data have become a sort of commodity to be traded with. The technology needed to exploit information and make a benefit of it already exists and continues to develop. This has major outcomes for personal privacy in the digital sphere. The lack of training in the way we use the network implies that sometimes we do not manage correctly our privacy in the Internet.
The top 50 countries with the biggest export volume in 2019 and 2020 were modeled and analyzed using complicated network analysis criteria in this study. In the year 2020, the COVID-19 pandemic has expanded globally. At this stage, it has been attempted to comprehend how Covid-19 influences the export flows of the nations included in the study. Graphs were built and processed using several techniques in the Gephi application. The positions of the countries in the network have been determined. Degree centrality, closeness centrality, and betweenness centrality criteria were examined among network statistics. Modularity analysis was used to determine the network's communities. The research also sought to introduce the Gephi software and demonstrate its many capabilities.
Since the Second World War, social network research on the structure of international commerce networks have been conducted. Different network analysis programs and techniques were used in the studies.
Serrano and Boguna (2003) conducted the first empirical study to characterize trade relations between countries as a world trade network. Serrano and Boguna (2003) conducted the first empirical analysis to define bilateral trade connections as a global trade network. Sui et al. (2021) evaluated the nations engaged in the Belt and Road Initiative over three time periods: value-added trade, real-trade, and value-added trade. They made use of the Gephi network analysis software. Based on 2018 data, Pacini et al. (2021) conducted a social network study of the worldwide plastic scrap trade. They used the Gephi tool to analyze the nations based on the centrality criterion in their study. As a result, they determined that the European Union and North American nations play a significant part in the plastic trash trade, whilst Latin American, African, and Eurasian countries play a little one. Sikos and Meirmanova (2020) explored the global import and export linkages of wheat commerce in their study. They applied several algorithms and statistical approaches from the Gephi SNA application. The modularity characteristic, as well as degree centrality, proximity centrality, and betweenness centrality criteria, were used to identify important nations in wheat trading. They came to the conclusion that the world's wheat supply trade is characterized by a scale-free nature. In their work, Amador et al. 2018 depicts global value chains. NODEXL, a network analysis tool, was employed for the analysis. As a consequence of network measures, they discovered that China, Germany, the United States, and Japan all play vital and distinct roles in international manufacturing. Germany is both a user and a provider, while the United States primarily serves as a supplier that adds value to other nations.
Wei et al. (2018) used SNA to evaluate the structure and growth of the Belt and Road Initiative's business relationship networks (Belt and Road Project). The UNICEF initiative was utilized. Changes in the placements of the nations in the network, as well as the variability of the modularity, have been noticed throughout time. Benedictis and Tajoli (2016) examined Italy's export market share and location in the global trade network in 1965, 1995, and 2011. They used network analytic methodologies to analyze the centrality criteria. Walther (2015) used SNA to investigate informal trading. They spoke about how social relationships between nations affect economic performance, how to utilize network analysis to disperse resources and information, and how network analysis affects commercial policy. Using the SNA approach, Aller, Ductor, and Herrerias (2015) assessed the environmental effect of the global commerce network. One of the findings of their research is that the presence of multinational corporations in developing nations has a negative impact on environmental quality.
With network analysis, Iapadre and Tajoli (2014) attempted to uncover the commercial flows of emerging nations and regions. They discovered that regionalization fell between 1995 and 2011, that each of the BRIC nations had a particular role in the area, that China acted as a hub in South-East Asia, and that Brazil, India, and Russia were regional racists. Deguchi et al. (2014) used SNA to study global commerce networks from 1992 to 2012. In their research, they demonstrated the shifts in time zones of nations that serve as global centres and authority. They discovered that the years when America and China were hubs and authorities varied, and that Europe's authority and hub values had dropped through time. Using the SNA approach, Wei and Liu (2012) disclosed their worldwide business networks from 1962 to 2000. Benedictis and Tajoli (2011) ) used network analysis to assess the change in the global commerce network. According to the findings of the study, global trade networks are still not fully integrated, there is variability in the choice of partners (business partners), and there exist regional clusters. Schiavo, Reyes, and Fagiolo (2010) used complex network analysis to investigate international trade and financial integration models. They discovered that international trade is more densely integrated than financial networks, and that high-income nations are more interconnected. Fagiolo, Reyes, and Schiavo (2010) used the network technique to analyse trade flows between nations. According to their findings, many nations have weak linkages with one another, but there is also a group of countries with strong ties, affluent countries have more intensive trade links, and these network features remain consistent over time.
Hafner-Burton et al. (2009) investigated whether approaches may be used to model international relations networks and what factors should be taken into account during network analysis. To assess the consequences of globalization, Kastelle, Steen, and Liesch (2006) investigated international trade networks between 1938 and 2003. According to the findings of the study, the international commerce network followed a steady route for a length of time. Kim and Shin (2002) used SNA to conduct a longitudinal analysis of commodities trade across nations and regions in their study. According to the findings of the study, the globe grew increasingly globalized between 1959 and 1996, with intra-regional links being stronger than regional ties (Caldarelli et al. 2012).
The study compared the trade flows of the top 50 nations with the greatest export level for 2019 and 2020. The relations were discovered using Gephi 0.9.2, a network analysis application. Gephi 0.9.2 is an open source network analysis application that may be used to uncover complicated relations. Gephi is more adaptable and has better visualization capabilities than other existing network software packages.
The nations export data were collected from the Trademap (trademap.org) website. The collected data were modified to the network structure and loaded into the Gephi application. The Gephi Program received a total of 8,992 data in 2019 and 8,933 data in 2020. It has been discovered that certain countries’ data contains errors or insufficient information. Such issues have frequently been discovered in developing or small countries. These data are limited in quantity and have been detected and cleaned.
Each nation is a "node" in the graphs, while export flows between countries are defined as "edges." Because each nations export flow is not reciprocal, the connections are weighted and directed. Directional-weighted graphs were constructed by using the export quantities as connection weights. Weighted graphs can provide more precise and through information.
Figure 1 depicts graph of nations based on total export quantities. The geo-based network was spatialized using Geo-Layout, a geographic layout method. Each nations’ geographic coordinates (latitude and longitude) were gathered and placed into the Gephi application. Node sizes and colors are also formed according to export sizes (the big and bright pink node has the most export amount). The nations having the greatest export levels, are China, America, and Germany, in that order. At first, it can be observed that America has a strong commercial flow with China, Canada, and Mexico, China has strong links with Hong Kong, Japan, and Korea, and Germany has strong relations with both China and America. Since the diagram for 2019 is not different from the diagram shown, it has not been redrawn. This algorithm allows us to see all countries according to the coordinates. This technique, however, does not allow us to obtain precise information. Starting from this algorithm, we can see that Germany, China and America carry out a very large part of the export volume of the whole World.
In Fig. 2, the nodes are positioned according to the Fruchterman-Reingold algorithm. The continents are used to categorize countries. Because the network has a large number of nodes and edges, the Fruchterman-Reingold layout is utilized, which distributes the nodes equally across the display and prevents the edges from overlapping. The Fruchterman-Reingold algorithm follows the force distribution law. This method groups and puts the highly linked nodes in the center of the graph, while scattering the less connected nodes about the network. The size of nodes varies depending on the amount of export. The fact that the nodes in the picture are grouped together in the center indicates that the export flow is between Asian and European nations. The low level of economic development of the countries in the African continent is also reflected in the graphs. South Africa is the only country on the African continent with the greatest export level. Chile, Brazil and Argentina from the South American continent are countries with high export levels. Although the export levels of the countries are different between 2019 and 2020, it is understood that the positions of the countries that are related to each other have not changed. With this algorithm, we can see the countries that have an intense relationship, and we can also distinguish the countries with low export volumes and those with little relationship. Although in large networks, it is not possible to see the one-to-one relationships of the nodes, it is convenient in terms of seeing the whole and positioning the nodes according to the power-law distribution.
In big networks, "filtering" is required to improve intelligibility and assure the emergence of active nodes. The Gephi program's filtering feature was utilized in Fig. 3. The average number of degrees was calculated for both graph and the connections below the mean were removed from the graphs. As a result, densely connected nodes evolved. Germany, USA and China meet a very large part of the export amount in the world. In terms of global commerce, it can be seen that Asian and European nations have more intense links with each other. European nations with high GDP and sophisticated economies are positioned to be concentrated in the center. Country with few connections are situated along the network's edges, which also reveals the economic status of the countries. When we compare 2019 and 2020, it is seen that although the export flow between countries decreased in 2020, the countries that steer the world economy did not change. The locomotive of Europe is Germany, followed by the Netherlands, France, Italy and England, respectively. It is an active participant in the trade network of North and South American nations such as America, Mexico, Canada, Argentina, Chile, and Brazil. The People's Republic of China has the largest export value among Asian countries and all countries in the globe. With the emergence of the Covid-19 virus in the People's Republic of China in 2019, it has developed into a pandemic that will engulf the entire planet by 2020. Despite this, China continues to be the country with the highest volume of exports. The deepening of the linkages between nations indicates a high level of commerce movement. As a result, Germany, along with many other nations in Europe and, notably, America and China; we can see the intensive commercial flows of the People's Republic of China to Hong Kong and the United States, as well as the United States to Mexico and Canada. Despite the fact that the analysis includes the commercial movements of 53 nations in South and North America, it is clear that the whole continent is exported through 6 countries. Geographic distance is obviously quite important in all of these movements. It is assumed that trade volumes between neighboring nations are increased as well. The countries' stronger neighborly ties and cheaper logistical costs are two of the most important factors for this.
Modularity Analysis
Community analysis is used to decompose clusters of highly connected nodes into several relatively independent modules (group, cluster, or community). Modularity is designed to measure the density of connections within communities as compared to links between communities (Zhigao et al. 2018). By means of modularity, information can be obtained about the level of regionalization in world trade.
In our study, the modularity optimization approach is used by the Gephi software. The determination of community structures gives a clue to us whether there is also regionalization. The Circle Pack Layout method was used to model community structures. This algorithm allows to determine what is wanted to be shown in the graph with hierarchical order.
In 2019, four trade communities developed, with five more to follow in 2020. Figure 3 shows the locations of these clusters. Each color symbolizes a different group. Within the community, each country is closely interconnected, although there are fewer regular linkages across communities. 4 trading communities emerged in 2019 and 5 in 2020. It contains Germany (purple color), which has the biggest community in 2019 and is surrounded by 75 nations. America (yellow color) and 46 nations around the tiniest settlement. Communities will continue to be led by China, Germany, and the United States in 2019 and 2020. The community headed by India, Saudi Arabia, and the United Arab Emirates in 2019 has shifted in 2020. India exited this commercial community and maintained its commercial activities in the cluster led by China (blue color). In addition, South African-led communities organized and developed a new business cluster in 2020. Although the economic structure of developed nations is solid and trade flows have not changed, we may claim that the Covid pandemic has established new trade avenues for poor countries. Indonesia and the Republic of Congo expanded their exports in 2020 and joined the China-based business community. It is interesting that there are two distinct business groupings in one society, where China is the focal point. In 2020, the number of nations in the trade flow, with China as the core, has risen. The Covid-19 virus emerged in China in 2019 and spread around the world in 2020. We may predict by 2020, China will have gained greater expertise in dealing with the outbreak and will have expanded its business flows in this area. As a result, by 2020, China will have initiated or expanded trade relations with a variety of nations. The number of nations with whom it has formed strong connections has not changed much in 2019 and 2020 under Germany's leadership. According to Wei and Liu (2012)’s analysis, Turkey is placed in the community in which Russia is the center. In 2019 and 2020, it takes place in a Germany-based community. This indicates that Turkey's commerce with European nations is improving, and that Russia is likewise emerging from a central commercial community. Although the number and communities of nations in trade flows focused on America, China, and Saudi Arabia have changed in 2020, there is essentially no change in the community structure in which Germany is the center, indicating that the countries with whom Germany deals have not changed. Usually, this is Europe and Russia. Despite the pandemic, the formation of such a picture indicates that Germany's economy is quite robust, and that it has strong relationships with the nations with which it has business dealings.
Iapadre and Tajoli (2014) talk about the existence of two opposite situations about regionalization in their study. First; the increase in regionalization in world trade. The second is the decrease in regionalization as a result of the decrease in transportation and communication costs, the increase in the number of commercial partners of the countries and the long-distance trade. Although both ideas require a detailed analysis, the first conclusion that can be drawn according to our study is the existence of regionalization.
Topological Characteristics of the World Trade Network
Table-1 depicts the World Trade method in terms of network analysis. According to the total quantity of exports, there is no notable change in the top 20 nations in 2019 and 2020. For both years, the top five nations with the largest export volume are China, America, Germany, Japan, and the Netherlands. Russia will have the greatest shift in export volume in 2020, with a 25% decline. France, England, and India have 17 percent each, while America and Canada have 15 percent each. While export volumes declined in several countries in 2020, the UAE with a 6% rise, Taipei with a 5% increase, China with a 4% increase, and Hong Kong with a 3% increase were among the nations that saw an increase.
One of the most significant network topological aspects is the degree distribution. It is a crucial criteria in large-scale networks for describing and understanding the network's distinctive structure. The degree distribution is a network criteria that specifies how many nodes in a network have particular degree. The force distribution (in this study) is the frequency distribution of the export volume values of the nations in the analyzed network. Figure 4 depicts the distribution of nodes based on export volumes. As a result, the World Trade network is seen to suit the scale-free distribution. Few nations have a large export volume, whereas many countries have a low export volume, based on the distribution of forces. In this situation, the nodes have a heterogeneous structure as opposed to a homogeneous structure.
With this distribution, we can see that the world is still far from being fully connected, but in some subregional components we can see interconnected nodes. This distribution is in parallel with the Benedictis and Tajoli (2011) study.
Centrality statistics are very useful in comparing the roles of nodes within the network. In this study, degree, closeness, and betweenness centralities were evaluated as centrality statics. Since each centrality criterion has different interpretations in a network, it should not be interpreted through a single centrality value. The measures of centrality that have been used attempt to assess how influential a country is within the international trading system as a whole. Since there are different interpretations of each centrality criterion in a network, it is not necessary to make sense of the nodes over a single centrality value, so different criteria have been evaluated.
A degree is the number of neighbors to which a node is linked. Degree centrality is defined by Freeman (1978) as the number of links that connect a node to other nodes. The simplest measure of centrality that measures the importance of nodes in a network is degree centrality. Degree centrality determines if a node is essential. The higher a node's degree, the more important it is in the network. Degree centrality in international trade is the number of countries a country exports to and imports. Therefore, the impact on the international trade network can be determined by degree centrality (Zhigao et al. 2018). According to Table 1, Germany, the Netherlands and the UK are the countries with the highest degree in 2019 and 2020. Although China has the largest export volume, degree centrality is low. This suggests that it has a greater volume of trade flows with certain countries.
The number of incoming connection to a node is referred to as the "indegree" in directed (asymmetric) networks, whereas the number of outbound connection from the node is referred to as the "outdegree." While Germany, the Netherlands, and the United Kingdom have the highest outdegree degree, certain nations, such as Austria and the United Arab Emirates, will not be among the top 20 in 2020. Even though Turkey's export volume fell in 2020, its outdegree centrality grew. This indicates that Turkey has begun to establish business with a broader range of countries.
Looking at the indegree centrality, economically weak nations rank first. Because the study only includes the 50 nations with the greatest export levels, the export data of many of the countries with high indegree centrality is missing. Greece and Bulgaria are the most noticeable European continent countries here. When the import and export quantities of both nations are reviewed, it is determined that imports exceed exports, resulting in a current account deficit. As a result of our research, it is possible to conclude that economic issues exist in many of the nations with a high indegree of centrality. Figure 2 shows that Greece and Bulgaria are placed away from the network center, towards the edges of the network which is validating our previous assumption.
Nodes with a high amount of indegree are referred to as "authority," whereas nodes with a high degree of output are referred to as "hubs." When determining authority and hubs, the HITS (Hyper-link Induced Topic Search) method is used. Kleinberg created this method for analyzing the network of web sites, and it is widely used in search engines, data and text mining. The HITS method is used by the Gephi application to do computations. Therefore, it is possible to calculate the authority and hub centrality. Nodes with high centrality of authority have a large number of connections from nodes with high centrality. Similarly, the high centrality of a node means that this node has outbound connections to many high-authority nodes (Newman 2010: 179). According to Table 1, the nations with the highest hub value in 2019, respectively, whereas Italy, the Netherlands, Germany, and India are the UK, the Netherlands, Germany, and Belgium in 2020. The hub value is proportional to the outdegree. When we look at the outdegrees we can see that there isn't much of a difference in the ranks. EU nations with a high hub value are also attracting attention. With their trade taxes and policies inside the EU territory, EU nations may gain from free trade. However, because countries outside the EU cannot profit from them, EU countries prefer to trade more with each other. (Deguchi et al. 2014) studies, while China has the world's largest hub value and the third largest authority value, in our study it is in the 19th place in terms of hub value in 2019 and is not even in the top 20 in 2020. This actually shows that China may switched to a different commercial strategy. It can also show that it has more commercial partnerships with certain countries. It shows that it has moved away from being the factory of the world in the year 2000 and has turned into a more world market.
Closeness centrality is an indicator of the distance of a node from other nodes (in terms of topological distance) and measures how easily a node can be reached by other nodes. It also means that an actor with high affinity has access to many other nodes and is therefore relatively independent of the control of others (Kilduff and Tsai 2003). The closeness centrality of a nation in the trade network relates to how much it is impacted by other countries and how much it is affected by other countries. As a result, after ranking first in 2019, Germany fell to second in 2020.
In betweenness centrality, the location of the node in the network is more essential than the number of nodes linked. Freeman (1977) defined it as the number of shortest paths between two nodes. If one actor is connected to other players in the network by a communication channel, that actor is the center. Actors having a high betweenness centrality may be able to influence how other actors communicate with one another. A more valuable network measure is betweenness centrality in trade network, which indicates how important a country is in terms of connecting other countries. Countries having a high betweenness centrality operate as a commercial bridge with other countries in the trade network. Betweenness centrality therefore quantifies the extent to which a certain node operates as an intermediate or gatekeeper in the network.
While Germany, Europe's locomotive, had the greatest value in 2019, it was surpassed by South Africa in 2020, with Germany taking second position. We can observe that European nations' centrality values are in the top 20 for both years. This might imply that European nations have diverse economic partners, as well as that there is a lot of commerce going on between them. The fact that Turkey is ranked among the top 20 nations in 2020 suggests that, in addition to its trade volume, it exports to other countries and serves as a bridge. A more significant organization measure is betweenness centrality, which demonstrates how significant a nation is as far as con-necting different nations. High betweenness centralities for the US, Turkey, and Germany demonstrate those nations are significant extensions between territorial business sectors.
The graphs of the betweenness centralities in Fig. 6 are drawn with the Openord algorithm. This layout s very useful to detect clusters. OpenOrd is basically Fruchterman-Reingold with an extra parameter (edge cut) that controls the greatest length of an edge during the optimization process (https://towardsdatascience.com). This layout is more suitable for large networks. The Openord algorithm has divided the graphs into 3 clusters. In 2019, the countries with high betweenness centrality in each cluster were Germany, UAE and Singapore, while in 2020 it was Germany, UAE and South Africa. Those countries are important bridges between regional markets.
If we evaluate the betweenness centrality over Table 1; While Germany, the locomotive of Europe, had the highest value in 2019, it was replaced by South Africa in 2020. We see that the centrality values of European countries are in the top 20 for both years. This may be an indication that European countries have trading partners in different regions. In the centrality of betweenness, Turkey's being among the top 20 countries in 2020 can mean that it exports to different countries and acts as a bridge.
The export relationship of the first 50 countries in the world according to their export level was analyzed with the SNA program Gephi. The aim of this study is to identify the countries that have an important role in the world by analyzing the trade relations between countries. In the study, degree, closeness and betweenness centralities from network statistics were evaluated. In order to detect the existence of commercial clusters in the world, a modularity analysis was performed.
It turns out that countries with high export levels often have specific trading partners. In this study, it has been concluded that commercial partnerships have not changed despite the worldwide crises such as the economic contraction of the countries or the pandemic that may affect all countries. According to Kim and Shin (2002) although this situation seems like a stable stance, increasing interconnectedness not only makes the world harmonious, but also makes it more unstable.
The United States of America was the largest authority in the global trade network after the second half of the 1990s, until China made a huge leap forward after 2000. China has significantly changed the world trade network, especially since 2001, by significantly increasing its trade volume and the number of trading partners (Deguchi et al. 2014). This rise has continued to the present day. Accordingly, it is understood from the size of the nodes that China has the largest export volume for both years. When we look at the whole picture, we can see that the countries that provide the economic order in the world are China, America and Germany. However, the downside of being important is that there are many countries that could be affected if they disappear from the network or in the face of any problems that may arise.
When we look at the export flows of the countries in the same region with each other, it is the European countries that have the most intense relations with each other. Countries in the African continent have the least interaction. Although Germany has the highest number of trade relations with the countries in the studies, it is the 3rd country in terms of export level and China is the country with the highest export amount. In this context, it has been concluded that the level of relationship as well as the level of export has an impact on the economic development of the countries.
As a result of the network analysis in the study, it was observed that the trade network exhibited a scale-free network and the network structure was heterogeneous. It cannot be expected that the world trade network will be fully connected due to the differences in the development level, economies, political structures, logistics network structures and resources of the countries. Considering that many of the real world networks are scale-free, our result is not surprising, but it is surprising that there has not been a huge change in relationships and network type, even with the Covid-19 epidemic that has affected the whole world. Zhigao et al. (2018) stated in his study, even in global economic crises, commercial relations may not be equally important for a country. Since we evaluated one year after the epidemic, it was observed that the trade partners of the countries did not change. In the coming years, it may be necessary to analyze the change of business partnerships and the effects of the pandemic over a longer period of time. Benedictis and Tajoli (2011) emphasized in their study that the world is far from being fully interconnected. It also shows that networks are “solid yet fragile” (Albert, Jeong, and Barabási 2000) although the power law has undeniable advantages in many cases. So it is resistant to random failure but vulnerable to targeted attacks. We live the best example of this in the current time period. Russia-Ukraine war caused many problems, as well as the products imported from Russia, especially gas-aluminum, iron-steel, etc. The embargoes imposed have also affected the economy all over the world. Russia is the country that feeds Europe in certain products. Such an embargo was of great interest to the whole of Europe and the USA. This is a very good example of the drawbacks of the power law.
We can see the importance of geographical proximity, especially from the trade relations of European countries with each other. In addition, it is revealed in the study that countries with lower export levels also export to countries close to them.
By using modularity feature in Gephi SNA program, commercial networks are divided into communities. It is divided into 4 communities in 2019 and 5 communities in 2020. It has emerged that the leading countries in the clusters are America, Germany, China, Saudi Arabia and UAE. Within the clusters, the geodetic distances of the countries from each other are striking. In the European continent, we can see that countries with high economic development or close to each other export with each other. It has been observed that intra-community connections are more intense than inter-community connections. Our findings show parallelism with the studies of Kim and Shin (2002) they have done.
In this study, the Gephi program was also tried to be introduced. Gephi is a very flexible, user-friendly program that also allows network statistics calculations. It has superiority over other network analysis programs in terms of having different algorithms and better reflecting the visuality of networks.
When we look at the effect of Covid − 19 on export flows in our study, almost every country except the Republic of China experienced a decrease in export levels in 2020. Despite this, export partnerships have not changed. Although there are variations according to the centrality characteristics, it may require longer-term analysis to clearly state that these effects are due to the pandemic. We will be able to see the economic impact of Covid-19 and the existence of economic partnerships better in the coming years.
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Tablo 1.
No competing interests reported.