知识网络中的链接预测和特征相关性:一种机器学习方法
Antonio Zinilli1, Giovanni Cerulli1
1IRCRES-Research Institute on Sustainable Economic Growth, CNR-National Research Council, Rome, Italy.
PloS one
|November 30, 2023
概括
我们开发了一个机器学习模型来预测联合研发项目的大学合作伙伴关系. 网络功能显著提高了预测准确度,突出了现有合作对未来合作的重要性.
科学领域:
- 社会科学 社会科学 社会科学
- 物理和工程科学 物理和工程科学
- 生命科学 生命科学
背景情况:
- 大学伙伴关系对于成功的联合研发至关重要.
- 视野2020计划资助跨多个科学领域的合作研发项目.
- 了解影响伙伴关系形成的因素是促进创新的关键.
研究的目的:
- 开发一个监督的机器学习模型来预测大学伙伴关系的形成.
- 分析网络 (内源性) 和非网络 (外源性) 特性对协作预测的影响.
- 确定推动成功联合研发项目形成的关键特征.
主要方法:
- 监督机器学习用于链接形成预测.
- 使用超级学习者部分效应和弹性进行特征重要性分析.
- 在两种情况下进行交叉验证准确性评估:使用所有特征和仅使用外部特征.
主要成果:
- 在包括网络和非网络功能时,实现了91%的预测准确性.
- 仅使用非网络功能时,预测准确度降至67%.
- 现有计划参与者 (现有者) 比新来者有24%的预测能力.
结论:
- 网络属性 (内生特征) 在预测大学伙伴关系方面最具影响力.
- 链接形成的概率随着特征的变化而降低,在属性和域之间均.
- 现有的合作关系显著提高了伙伴关系形成模型的预测能力.
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