A multiresolution framework for the analysis of community structure in international trade networks

Abstract

International trade networks are complex systems that consist of overlapping multiple trade blocs of varying sizes. However, the resulting structures of community detection in trade networks often fail to accurately represent the complexity of international trade. To address this issue, we propose a multiresolution framework that integrates information from a range of resolutions to consider trade communities of different sizes and reveal the hierarchical structure of trade networks and their constituent blocks. In addition, we introduce a measure called multiresolution membership inconsistency for each country, which demonstrates the positive correlation between a country’s structural inconsistency in terms of network topology and its vulnerability to external intervention in terms of economic and security functioning. Our findings show that network science-based approaches can effectively capture the complex interdependencies between countries and provide new metrics for evaluating the characteristics and behaviors of countries in both economic and political contexts.

Modular Structures of Trade Flow Networks in International Commodities

“…The trade networks constructed using the personalized Page Rank compared to the direct trade network [29]. Cho et al proposed a multiresolution framework that integrates information from a range of resolutions to consider trade communities [27]. These results differ from current works because they do not employ the optimized Louvian algorithm.…”

“…They detected some measures to decrease the properties of network over time. Cho et al proposed a multiresolution framework that integrates information from a range of resolutions to consider trade communities [27]. The global trade networks were analyzed using exponential random graph models [28].…”

Synchronization in simplicial complexes of memristive Rulkov neurons

“…The study of complex systems has yielded essential insights into the structures and dynamics of numerous interconnected systems, ranging from social networks (Shahal et al, 2020 ) and biological systems (Ma and Tang, 2017 ) to network marketing and trading networks (Kim et al, 2006 ; Feng et al, 2014 ; Cho et al, 2023 ), in the discipline of network science. Networks have traditionally been depicted as graphs, with nodes and edges capturing entities and their pairwise connections (Burgio et al, 2020 ; Ghosh et al, 2022 ).…”