通过异质图形卷积网络预测同词链接.
Yangmin Li1, Xin Zhang1, Xin Bai2
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130000, China.
Scientific reports
|July 2, 2025
概括
本研究引入了一种新的异质图形卷积网络 (GCN) 用于共同词分析,增强研究趋势发现. GCN模型有效地预测了共词网络中的链接,优于传统方法.
科学领域:
- 信息科学 信息科学 信息科学
- 图书馆科学 图书馆科学
背景情况:
- 同词分析通过检查术语的同时出现来确定研究主题和网络.
- 机器学习,特别是在同词网络中的链接预测,有助于发现研究相互作用和新兴趋势.
- 现有的方法难以同时学习共词网络中的单词共发生和单词-文档关系.
研究的目的:
- 提出一个端到端的深度学习模型用于co-word网络分析.
- 共同学习来自共词网络的词和文档嵌入,并结合特定文档信息.
- 改善研究主题之间的潜在相互作用的预测,并识别新兴趋势.
主要方法:
- 一个异质图形卷积网络 (GCN) 模型被开发用于同词网络分析.
- 该GCN模型通过结合特定文档信息,共同学习文字和文档嵌入.
- 该模型使用二进制标签进行训练,表明存在同词链接.
主要成果:
- 提出的基于GCN的方法实现了AUC值为[公式:参见文本].
- 这显著优于最好的传统机器学习方法,其AUC值为[公式:参见文本].
- 在信息科学和图书馆科学中的Web of Science数据集上进行了实验.
结论:
- 不同质的GCN模型有效地捕捉了共同词网络中的单词共发生和单词-文档关系.
- 这种方法为发现研究趋势和相互作用提供了一个强大的工具.
- 这些发现表明,深度学习模型的优越性超过传统方法在共同词的网络分析.
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