The triangular structure beyond pairwise interactions affects the robustness of the world trade networks

Area of Science:

• Network science
• International trade analysis
• Complex systems

Background:

• Traditional network analysis often overlooks higher-order interactions.
• Understanding the robustness of world trade networks is crucial for global economic stability.
• Filled triangles (two-simplexes) represent fundamental group interactions in complex systems.

Purpose of the Study:

• To analyze the impact of higher-order interactions on world trade network robustness.
• To construct and examine two-simplex trade networks using multilateral trade agreements and import-export data.
• To identify factors influencing trade network resilience over time.

Main Methods:

• Construction of two-simplex higher-order trade networks integrating multilateral regional trade agreements and world trade data.
• Topological analysis of network evolution, including scale, complexity, and changes in 2020.
• Node attack simulations to assess network robustness against key node removal.
• Generation of synthetic trade networks using random simplicial complex (RSC) and scale-free simplicial complex (SFSC) models.

Main Results:

• World trade networks exhibit significant growth in scale and complexity, with a decline in 2020.
• Network robustness generally improves with size and complexity but decreased in 2020.
• Increasing average degree enhances robustness; increasing nodes or filled triangles alone can weaken it.
• Scale-free networks show lower robustness due to hub vulnerability compared to random simplicial complexes.

Conclusions:

• Multilateral interactions and strengthened ties are vital for enhancing the robustness of world trade networks.
• Network structure, particularly the balance between higher-order interactions and node connectivity, dictates resilience.
• The findings provide insights into safeguarding global trade against disruptions.