用网络分析和潜在空间表示法调查国内移民：在土耳其的应用
Investigating internal migration with network analysis and latent space representations: An application to Turkey
摘要：
人类移徙模式影响到人口特征在地理上的重新分布，由于这种分布与社会和经济结果密切相关，因此调查国内移徙的结构和动态对于理解和设计这种系统的政策起着至关重要的作用。我们对2008年至2020年土耳其国内移民的结构和动态进行了深入调查。我们确定了一组经典的迁移规律，并通过各种方法进行检查，签署网络分析，自我网络分析，表示学习，时间稳定性分析，社区发现，和网络可视化。研究结果表明，与经典的移民规律，大多数移民联系的地理范围内有几个例外，涉及大的经济活动的城市，主要的移民流与移民流在相反的方向，有明确的移民路线，和移民系统是一般稳定的调查期间。除了这些一般性的结果，我们还提供了对土耳其的独特和具体的见解。总的来说，我们在文献中首次采用的新工具集允许从复杂网络的角度调查选定的迁移规律，并为未来不同地理区域的迁移研究提供了启示。
英文摘要：
Human migration patterns influence the redistribution of population characteristics over the geography and since such distributions are closely related to social and economic outcomes, investigating the structure and dynamics of internal migration plays a crucial role in understanding and designing policies for such systems. We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020. We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, community detection, and network visualization. The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity, major migration flows are countered with migration flows in the opposite direction, there are well-defined migration routes, and the migration system is generally stable over the investigated period. Apart from these general results, we also provide unique and specific insights into Turkey. Overall, the novel toolset we employ for the first time in the literature allows the investigation of selected migration laws from a complex networks perspective and sheds light on future migration research on different geographies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
