用贝叶斯惊喜检测音乐影响力随时间的突变节点。
Surprising Patterns in Musical Influence Networks
- 用贝叶斯惊喜方法追踪音乐网络演化
- 发现艺术家影响与采样网络存在显著结构突变期
- 框架可灵活验证多种演化假设，适合音乐数据研究者
分析音乐影响力网络（如艺术家影响或采样关系）已为当代西方音乐提供重要洞见。现有计算方法如中心性排序能识别关键艺术家，但对影响力随时间演变的关注较少。本文应用贝叶斯惊喜（Bayesian Surprise）来追踪音乐影响力网络的动态变化。基于两个网络——艺术家影响网络与翻唱、混音、采样网络——结果揭示了网络结构中多个显著的阶段性转变。此外，我们证明贝叶斯惊喜是一种灵活框架，可用于在真实数据上检验多种关于网络演化的假设。
原文摘要 · Abstract (English)
Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. However, little attention has been given to how influence changes over time. In this paper, we apply Bayesian Surprise to track the evolution of musical influence networks. Using two networks -- one of artist influence and another of covers, remixes, and samples -- our results reveal significant periods of change in network structure. Additionally, we demonstrate that Bayesian Surprise is a flexible framework for testing various hypotheses on network evolution with real-world data.
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