无监督的轨迹嵌入捕捉了科学迁移的潜在结构
Dakota Murray1,2, Jisung Yoon1,3,4, Sadamori Kojaku1
1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408.
概括
像word2vec这样的神经嵌入模型现在可以代表复杂的人类迁移模式. 这项技术捕捉了功能距离和关系,为科学移动性和更远的领域提供了新的见解.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 流动性研究 流动性研究
背景情况:
- 人类迁移是社会变革的关键驱动力,受到距离,语言和文化的影响.
- 传统的方法难以捕捉现代迁移模式的复杂性.
研究的目的:
- 证明神经嵌入模型的实用性,特别是word2vec,用于分析人类迁移.
- 创建一个有意义的向量空间代表迁移轨迹.
- 探索这些方法对科学迁移模式的应用.
主要方法:
- 将word2vec模型应用于300万科学家迁移轨迹的数据集.
- 利用word2vec的语义结构来识别迁移驱动因素.
- 利用分析模型与移动性的重力模型数学等价性的技术.
主要成果:
- word2vec生成了准确,密集和连续的迁移矢量表示.
- 该模型捕获了微妙的功能距离和位置之间的关系.
- 分析揭示了影响科学迁移的文化,语言和声望因素.
结论:
- 神经嵌入提供了一个强大的框架来代表和理解迁移.
- 这种方法为移民数据提供了"数字双重",使得进一步分析成为可能.
- 该方法适用于学术界内外的移民研究.
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