With 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.

中文翻译：

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考虑拓扑聚类的全球粮食贸易多层网络时空分析

随着全球化进程不断加快，全球粮食贸易网络结构日益复杂。传统的网络分析方法在捕获数据中的这些动态变化和隐藏的拓扑结构方面存在一定的局限性。本研究基于1988年至2022年全球水稻、小麦和玉米进出口贸易数据，提出了一种基于拓扑数据分析的时态多层网络拓扑聚类新方法，以系统评估水稻、小麦和玉米的拓扑结构演化。时间多层网络。结果表明，不同年份的农产品贸易网络呈现出隐藏的聚类特征。此外，本研究结合景观生态学原理，构建粮食贸易网络动态群落时空变化模型，旨在全面揭示粮食贸易网络的潜在模式和动态趋势，为粮食贸易决策提供有价值的信息。