Surprising Patterns in Musical Influence Networks

TL;DR

This paper uses Bayesian Surprise to analyze how musical influence networks evolve over time, revealing significant periods of change and demonstrating the method's flexibility for hypothesis testing.

Contribution

It introduces Bayesian Surprise as a novel approach to study temporal dynamics in musical influence networks, filling a gap in previous static analyses.

Findings

Identified key periods of structural change in influence networks

Demonstrated Bayesian Surprise's effectiveness in hypothesis testing

Applied method to artist influence and sampling networks

Abstract

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.