Affiliation:
1. School of Resources and Environment Anhui Agricultural University Hefei China
2. International School Beijing University of Posts and Telecommunications Beijing China
3. Huangshan Meteorological Bureau Huangshan China
4. School of Resource and Environmental Sciences Wuhan University Wuhan China
Abstract
AbstractWith accelerating globalization, the complexity of the global grain trade network structure is increasing. Traditional network analysis approaches have certain limitations in capturing these dynamic changes and hidden topological structures in data. Based on global import and export trade data for rice, wheat, and corn from 1988 to 2022, this study has proposed a novel method for the topological clustering of temporal multilayer networks based on topological data analysis in order to systematically assess the topological structure evolution of temporal multilayer networks. The results indicate that different agricultural trade networks reveal hidden clustering characteristics in different years. In addition, this study combines principles from landscape ecology to construct a dynamic community spatiotemporal change model of grain trade networks, aiming to comprehensively reveal potential patterns and dynamic trends in grain trade networks and provide valuable information for grain trade decision‐making.
Funder
Natural Science Foundation of Anhui Province