Analysis of the global trade network using exponential random graph models

Global trade of renewable energy products has increased significantly in recent years. This paper constructs an analytical framework of a global trade network for renewable energy products based on bilateral trade data between 2009 and 2019. It analyses its structural evolution at the global and local levels and investigates the influencing factors of the network with the Exponential Random Graph Model. The empirical results indicate that countries in the trade network have become more closely connected, featuring a core-periphery and increasing reciprocity relationship. China, Germany, and Japan have remained in the position of core countries; China has especially been prominent among core countries. Our empirical results verify that the sender-receiver effects can explain the evolution of this global trade network. The empirical results also indicate that the climate change agreement network and the common border network have positive effects on the formation of the trade network. As regards political implications, the core countries in the trade network should optimize the layout of renewable energy development and improve infrastructure accordingly. Countries should also jointly build a more fair and reasonable multilateral system that fulfills their responsibilities.

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety.

The global food and agricultural trade network is crucial for food security. Shocks such as those posed by extreme weather events, conflicts, pandemics, and economic crises can test the resilience of the trade network to the sudden interruption of trade flows. Depending on the level of connectivity in the trade network and its structure, such shocks have the potential to propagate through the entire network and can affect countries’ food availability and variety. This paper contributes to the literature on food and agricultural trade networks in two main ways: (1) understanding the global trade network as a complex system that can be affected by and responds to shocks, we define and operationalize its resilience as a multidimensional concept, which is shaped by the interdependencies in the network and their structure; and (2) applying techniques from network analysis to examine the evolution of three dimensions of resilience within the global food and agricultural trade network between 1995 and 2019. The main findings show that, between 1995 and 2007, trade connectivity among countries increased. Overall, this bolstered countries’ and the network's resilience to trade shocks. However, vulnerabilities persisted in terms of ensuring sufficient product variety and quantity. Adding to these vulnerabilities, trade integration stalled in the second half of the series, pointing to a slight tendency towards trade disintegration and potentially lower resilience of countries to trade shocks already in 2019.

In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements’ dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d’Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

The world is currently facing the worst migration crisis on record. Violent conflicts around the globe have forced over 65 million people to flee their homes, and receiving countries are struggling to support the massive influx of refugees. Lack of preparation and disorganization have only worsened the situation, and there is a pressing need to better understand refugee migration patterns in order to inform policy decisions and improve humanitarian efforts. In previous migration research, gravity models have been one of the classical methods for investigating determinants of migration, however this approach fails to take into account the interdependent nature of migration. To address this weakness, we apply statistical network analysis, which takes into account this interdependency, in order to quantify the influence of certain economic, political, social, and geographical factors on refugee migration. We create four different networks in order to investigate forced migration patterns surrounding four countries that are currently experiencing violent conflicts: Syria, Ukraine, the Democratic Republic of Congo (DRC), and Myanmar. Each network includes 12 nodes: the respective country of interest and the eleven countries hosting the most refugees from that country in 2015. Weighted directed edges represent the number of refugees from the origin country living in the host country in 2015. In order to quantify the influence of chosen factors on refugee migration in the context of the specific countries and conflicts of interest, we apply two different network models to each of the four networks —the exponential random graph model (ERGM) adapted for binary edges, and the generalized exponential random graph model (GERGM) adapted for weighted edges. Our results indicate that the ERGM is a poor choice for modeling this specific problem since the thresholding required to coerce weighted edges into binary edges results in a failure to capture the vastly different magnitudes of refugee migration present in the networks. The GERGM proved to be a much better model. Our final GERGM produced vastly different results for each of the four networks, suggesting that refugee migration patterns differ greatly for different countries and conflicts. Our results also suggest that the influence of determinants of migration on refugee flow patterns differs greatly for outmigration and in-migration. We speculate that determinants of migration have a greater influence on out migration than in-migration in the context of more recent conflicts. On the other hand, we speculate that determinants of migration have an influence on both out-migration and

Uncovering the features of global antimony resource trade network

In the context of escalating geopolitical tensions, recurring aircraft safety incidents, and frequent unforeseen events, the security of aircraft supply faces significant challenges. This research employs complex network theory to analyze the evolutionary characteristics of three global aircraft trade network (GATN) systems from 2015 to 2024. It then applies the entropy-weighted TOPSIS method to assess node importance within the network and finally conducts a robustness analysis based on the node importance ranking. The results indicate that the number of participating countries has declined post-pandemic, while trade concentration has increased. Analysis of the node’s importance reveals that the United States holds the most critical role in the GATN. The global medium aircraft trade network is characterized by one dominant player alongside several strong competitors, whereas the global large aircraft trade network features multiple major players coexisting. Regarding network robustness, targeted node attacks cause significantly more disruption than random node attacks. After removing 10% of key nodes, the global small aircraft trade network’s average connectivity fell to 0.6, and efficiency dropped to 0.1. Similar patterns were observed in the medium and large aircraft networks, with connectivity decreasing to 0.4 and efficiency to 0.05. Under targeted attacks, the global small aircraft trade network is more robust than the medium and large ones. This study provides quantitative insights to help optimize aircraft trade strategies.

Advances in exponential random graph ( p*) models applied to a large social network

As a critical raw material in the semiconductor and new energy sectors, antimony is a strategic mineral resource for nations to safeguard industrial chain security. However, the scarcity of its resources and the complexity of its trade pattern underscore the urgency of antimony-related research. This study aims to reveal the structural characteristics of the global antimony trade network and explore the external factors influencing trade. Based on global antimony trade data from 2007 to 2022, the characteristics of the antimony trade network were analyzed using the complex network analysis method, and the influencing factors of antimony trade were examined via the fixed effects model. The results show that the global antimony trade network maintains a density of 0.05–0.06, with an average path length of 2.4–2.7 and a network diameter that mainly fluctuates between 5 and 6. The average clustering coefficient fluctuates within the range of 0.35–0.45. Overall, the network exhibits the characteristics of stable transmission efficiency, loose overall connectivity, and local agglomeration without a consistent upward or downward trend. Countries such as Germany, China, and the United States occupy core positions in the network. The fixed effects model indicates that GDP and LOGISTICS development are key factors promoting trade, while TARIFFS and REGULATORY policies have a significant inhibitory effect on trade. Therefore, ① Focus on the High-End Development of the Antimony Industry Chain and Promote the In-Depth Integration of Antimony Trade with the Semiconductor and New Energy Industries; ② Improve the Cross-Border Logistics and Warehousing System for Antimony Trade to Ensure the Efficient Circulation of Strategic Resources; ③ Promote; Promote Tariff Liberalization in Antimony Trade and Eliminate Market Access Barriers; ④ Strengthen the Government’s Strategic Support for the Antimony Industry to Enhance Global Discourse Power in Antimony Trade; Trade; ⑤ Maintain Macroeconomic Stability and Flexibly Manage Exchange Rates to Safeguard the Resilience of Antimony Trade.

With the rise and fall of the trade shares of different countries in the world, does the trade network structure change at the same time? Do the dynamics of countries' positions differ in the evolution of trade network structure? Based on the latest world input-output database (WIOD), this paper illustrates the accounting models of international trade, and describes the dynamics of the global trade network structure and the countries' positions. Research shows that China and the emerging countries developed faster than the developed countries during 2000-2014, and play important roles, not only in the trade shares, but also in global trade networks. The innovation of this study is that we present a systemic and explicit portrait of the global pattern of trade linkages between countries based on a set of social network analysis methods, and we find that the dynamic of linkage network is more violative than that of linkage flow.

Purpose In an era of intensifying climate change, geopolitical conflicts, and economic volatility, ensuring the stability of food supply chains is essential for food security. This study investigates a critical yet underexplored question: When subjected to diverse external shocks, do global food trade networks possess the necessary resilience and adaptability to maintain functional import channels for nations? Design/methodology/approach Utilizing trade data from 2000 to 2022, we construct and analyze four distinct global food trade networks: wheat and meslin, maize, rice, and a composite total food network. We employ a novel, multi-dimensional analytical framework that integrates the structural evolution of networks, node-level resilience, and structural resilience. The robustness of these networks is rigorously tested through simulations of both random and deliberate strategies, targeted attacks on core nodes. Findings Our analysis reveals that while global food trade networks are expanding and becoming more interconnected, their resilience is unevenly distributed. Countries like Japan, South Korea, Iran, and China are reliant on imported food and exhibit trade vulnerability due to concentrated import sources. Major food traders, including Canada, Mexico, China, and South Korea, serve as hub nodes of the network, alongside smaller trading nations like South Africa, Denmark, and Belgium. Among the four types of food trade networks, network connectivity is strongest for wheat and meslin, whereas the rice trade network displays the highest structural stability. Deliberate attacks are more detrimental to network resilience than random attacks, with the failure of core nodes such as France and Germany significantly exposing a critical systemic vulnerability. Originality/value Moving beyond prevailing research that often focuses on single shock types or aggregate networks, this paper provides a nuanced, multi-network and multi-strategy analysis of trade resilience. It introduces an integrated node-structure framework for assessing systemic risk. The findings offer actionable insights for policymaker, emphasizing the urgency of diversifying import sources, strengthening regional trade cooperation, and developing differentiated contingency plans based on specific network roles and shock characteristics.

Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

IntroductionWith the development of global industrialization, metal minerals have become a global focal point of great power competition. An in-depth investigation into the evolution of global mineral trade structures, alongside an analysis of the relationships between metal minerals trade, resource endowments, industrialization, regional dynamics, and geopolitical competition, is crucial for nations to formulate effective trade policies and enhance the stability of global mineral trade development.MethodsThis study explores the evolution trends of the global metal minerals trade structure from 1990 to 2022 based on complex network and further detect community structure using the Infomap algorithm.Results and discussionThe results show that (1) There is a general upward trend in global metal minerals trade from 1990 to 2022, which can be segmented into phases of slow, rapid, and moderate growth. (2) The two major trading circles formed in the early stage with Japan and Europe as the center have changed, forming a dual pattern with China as the super demand center and Australia as the super supply center. (3) China, Australia, the U.S., Japan, Brazil, and the European Union play key roles in shaping the global trade network, with the structure of the global metal minerals trade network primarily being driven by demand centers. (4) According to the economic trends and the evolving resource demands, the global trade structure will translate from a concentrated bipolar model to a diversified network with multiple trade centers. The conclusion of this research helps specify international policies and maintain supply chain resilience.

This article bridges two important approaches in the study of global trade – social network analysis and the gravity model – and examines how countries’ structural locations in the global trade network influence bilateral trade from 1948 through 2000. The authors identify a cohesion effect of structural equivalence (the degree to which two nodes have similar ties with other nodes in the network) in global trade: two structurally equivalent countries develop more bilateral trade even after controlling for conventional dyadic factors. This is because common trading ties with other countries promote similar sociocultural values, information flows, and converging institutions, thereby boosting bilateral trade. The authors further demonstrate that the cohesion-generating role of structural equivalence has become more salient over time in the increasingly complex global trade network. Overall, this study shows that bilateral trade is embedded within the structural context of the overall global trade network, and this structural effect is historically contingent on the evolving nature of global economic activities.

Characteristics of the global copper raw materials and scrap trade systems and the policy impacts of China's import ban