基于多维特征的绿色专利合作网络的链接预测
Mingxuan Yang1, Xuedong Gao1, Yun Ye1
1School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一个新的多维链接预测模型,用于区域绿色专利合作网络. 该模型提高了预测准确性,帮助组织识别潜在的技术合作伙伴.
科学领域:
- 创新研究 研究 创新研究
- 网络科学 网络科学
- 环境政策 环境政策
背景情况:
- 区域绿色专利合作网络对于理解协作创新至关重要.
- 这些网络中的链接预测可以预测趋势,并确定技术合作伙伴.
研究的目的:
- 为区域绿色专利合作网络提出一个多维链接预测模型.
- 集成节点,路径和内容功能,以提高预测准确度.
- 将该模型应用于北京-天津-河北地区,以获得实际见解.
主要方法:
- 开发了一个集结节点,路径和内容特征的模型.
- 使用权方法来计算节点相似性指标.
- 包含异质路径分析和专利文本主题分析,以确定内容相似性.
- 使用灰狼优化器 (GWO) 来确定最佳重量.
主要成果:
- 与现有方法相比,多维预测模型显著提高了预测准确性.
- 实验结果验证了模型在预测网络演变中的有效性.
- 该模型成功预测了北京-天津-河北地区的绿色专利合作网络.
结论:
- 拟议的多维链接预测模型为分析和预测绿色专利合作网络提供了强大的方法.
- 准确的链接预测有助于组织识别技术进步的战略合作伙伴.
- 这些发现为区域创新动态和合作模式提供了有价值的见解.
相关概念视频
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01:24Sequence Networks of Rotating Machines
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