用嵌套图分析欧歌赛胜出歌曲的投票与音乐特征关系
Exploring ESC Winners with Nested Diagrams
- 基于形式概念分析构建嵌套线图，融合多值属性与概念尺度
- 揭示1975-2025年欧歌赛胜出曲目的投票偏好与音乐节奏、调性关联
- 适合数据可视化与社会文化数据分析研究者使用
我们提出 ConceptFlow，一个兼容 scikit-learn 的 Python 库，用于形式概念分析（FCA），能够从多值形式上下文构建并渲染嵌套线图。给定一个多值上下文及其属性划分到概念尺度，ConceptFlow 执行概念标度、计算因子格、识别子直积中的填充节点，并生成交互式可视化。我们将该方法应用于 1975 至 2025 年欧歌赛获奖作品，探索投票模式与音乐特征之间的关系。外层尺度涵盖区域、文化、历史和政治维度的投票支持，内层尺度通过节奏（tempo）和调性（key）捕捉音乐特征。生成的嵌套线图揭示了两尺度间的蕴含关系，暴露了获胜歌曲的得票方式与其共享音乐属性之间的依赖性。
原文摘要 · Abstract (English)
We present ConceptFlow, a scikit-learn-compatible Python library for Formal Concept Analysis that constructs and renders nested line diagrams from many-valued formal contexts. Given a many-valued context and a partition of its attributes into conceptual scales, ConceptFlow performs conceptual scaling, computes the factor lattices, identifies filled nodes of the corresponding subdirect product, and produces an interactive visualization. We apply ConceptFlow to the winners of the Eurovision Song Contest from 1975 to 2025, exploring relationships between voting patterns and musical characteristics. Voting support is captured by an outer scale spanning regional, cultural, historical, and political dimensions, while an inner scale captures musical characteristics via tempo and key. The resulting nested line diagram reveals implications across both scales, exposing dependencies between how winning entries were voted for and the musical properties they share.
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