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首页> 外文期刊>EPJ Data Science >Tracing patterns and shapes in remittance and migration networks via persistent homology
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Tracing patterns and shapes in remittance and migration networks via persistent homology

机译:通过持久的同源追踪汇位和迁移网络的追踪模式和形状

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Pattern detection in network models provides insights to both global structure and local node interactions. In particular, studying patterns embedded within remittance and migration flow networks can be useful in understanding economic and sociologic trends and phenomena and their implications both in regional and global settings. We illustrate how topo-algebraic methods can be used to detect both local and global patterns that highlight simultaneous interactions among multiple nodes, giving a more holistic perspective on the network fabric and a higher order description of the overall flow structure of directed networks. Using the 2015 Asian net migration and remittance networks, we build and study the associated directed clique complexes whose topological features correspond to specific flow patterns in the networks. We generate diagrams recording the presence, persistence, and perpetuity of patterns and show how these diagrams can be used to make inferences about the characteristics of migrant movement patterns and remittance flows.
机译:网络模型中的模式检测为全局结构和本地节点交互提供了见解。特别是,嵌入在汇款和迁移流网络中的学习模式可用于了解经济和社会学趋势和现象以及它们在区域和全球环境中的影响。我们说明了Topo-AlgeBraic方法如何用于检测突出多个节点之间突出同时交互的本地和全局模式,在网络结构上提供更全面的透视图以及定向网络的整体流动结构的更高订单描述。使用2015年亚洲净迁移和汇款网络,我们构建并研究其拓扑功能对应于网络中特定流模式的相关指导的Clique复合体。我们生成录制模式的存在,持久性和永久性的图表,并展示这些图表如何用于制作关于移民运动模式和汇款流的特征的推断。

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