Communities in world input-output network: Robustness and rankings

Area of Science:

• Economics
• Network Science
• Data Analysis

Background:

• Community detection is crucial for understanding complex systems like global economies.
• Assessing the robustness and stability of these detected communities is essential for reliable analysis.
• Previous methods lacked robust ways to evaluate the stability of economic networks.

Purpose of the Study:

• To introduce a novel method for assessing the robustness of community detection in networks.
• To apply this method to the World Input-Output Network (WION) for economically plausible insights.
• To identify key economic communities and track their evolution over time.

Main Methods:

• Development of the algorithmic assignment variability index to measure community stability.
• Application of centrality measures on a synthetic network of WION communities.
• Analysis of community rank evolution and its drivers from 2000-2014.

Main Results:

• The proposed method successfully ranked communities in the WION by robustness and stability.
• The algorithmic assignment variability index demonstrated predictive power for community rearrangement.
• Identified decreasing trends in US and Japan-centered communities, and a rising trend in China-centered communities.
• Observed Germany-centered community growth attributed to the inclusion of the Netherlands and Belgium.

Conclusions:

• The new method provides a robust framework for analyzing economic network stability.
• Community detection robustness is a valuable metric for understanding dynamic economic interdependencies.
• The study reveals significant shifts in global economic influence, with China's prominence increasing.