SilhouetteScoreinR:超越传统的网络布局,利用当地凝聚力和最近邻居的隔离
Hua-Ying Chuang1,2,3, Willy Chou4,5
1Department of Internal Medicine, Chi Mei Medical Center, Chiali District, Tainan, Taiwan.
MethodsX
|September 29, 2025
概括
轮分数 (SS) 通过评估同词网络和作者合作中的集群质量来增强文献识别分析. 该方法识别了连贯的集群和异常值,改善了数据解释.
科学领域:
- 图书统计学 图书统计学
- 数据科学数据科学数据科学
- 网络分析 网络分析
背景情况:
- 轮分数 (Silhouette Score,简称SS) 是评估聚类质量的指标,通常用于数据科学,但在文献计量学中未得到充分利用.
- 传统的文献计量网络分析往往会模糊边界或错误分配的实体,限制可解释性.
研究的目的:
- 介绍和展示 siluette 评分的应用,以评估图书识别网络中的集群质量.
- 提供一种新的可视化框架,将轮分数与出版物计数相结合,以提高可解释性.
- 提供R脚本,用于计算图形得分在文献计量分析.
主要方法:
- 应用轮得分分析对2,252个MethodsX文章 (2020-2024) 的数据集.
- 使用R脚本计算SS,用于显式和隐式网络布局 (例如PCA,UMAP).
- 开发了一种双轴可视化图谱,将出版物数量与轮分数进行对比.
主要成果:
- 轮分数有效地评估在共同词和作者协作网络中的聚类质量,独立于聚类算法.
- 新型可视化突出显示连贯的集群 (高SS) 并识别异常值或错误分配的实体 (低/负SS).
- 该框架提高了术语,集群和语料库层面的解释性,有助于标记和进一步分析.
结论:
- 轮分析提供了一种强大的方法来评估文献计量学中的集群质量,补充了传统的网络可视化.
- 拟议的可视化框架提供了对网络结构和实体赋值的更细致的理解.
- 这种方法支持在文献识别研究和相关数据分析领域做出更有辩护力的决定.
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