Two-Dimensional Structural Characterization of Music Genre Communities in Playlist Co-occurrence Networks

TL;DR

This paper introduces a two-dimensional, data-driven framework to analyze music genre communities based on playlist co-occurrence networks, capturing boundary strength and internal differentiation without relying on fixed labels.

Contribution

It operationalizes sociological genre dimensions from consumption data, revealing complex genre structures and evolution beyond traditional label-based classifications.

Findings

Genre boundaries and internal structures are statistically independent.

The framework uncovers genre splits and merges invisible to fixed labels.

Hip-Hop shows unexpected internal differentiation, challenging existing views.

Abstract

Music genre classification shapes how listeners discover music, how platforms design recommendations, and how sociologists study cultural taste. Yet existing genre labels are inconsistent in granularity: they exaggerate boundaries between overlapping categories and hide sociologically important heterogeneity within broad labels. Cultural sociologists have long theorized that genres vary along two independent dimensions, boundary strength and internal differentiation, but existing empirical work has relied on fixed label sets, leaving these dimensions without quantitative operationalization from actual consumption behavior data. Here we propose a two-dimensional framework that extracts music communities bottom-up from playlist co-occurrence networks and characterizes each along two axes: external closure $B(C)$, measuring boundary strength relative to a random null, and internal differentiation $D(C)$, measuring organized internal subdivision. We validate the framework on two independent datasets across platforms, cultural contexts, and time periods, confirming that $B(C)$ and $D(C)$ are statistically independent and that each captures a distinct structural property. The framework reveals genre structures invisible to fixed labels: single labels splitting into communities with different boundary strengths, multiple labels merging into tightly bounded communities, and consumption spheres that no existing label describes. Comparison with prior theoretical predictions is broadly consistent, with the notable exception that Hip-Hop exhibits rich internal differentiation across both datasets, challenging its prevailing single-centered characterization. By providing a label-independent coordinate system grounded in listener behavior, this framework opens a path toward tracking how genre boundaries and internal structures evolve over time, a question that static label systems cannot address.