Peace Sells, But Whose Songs Connect? Bayesian Multilayer Network Analysis of the Big 4 of Thrash Metal

TL;DR

This paper introduces a Bayesian multilayer network analysis framework applied to the discographies of the Big 4 thrash metal bands, revealing structured connectivity and meaningful musical patterns from raw audio features.

Contribution

It develops a novel Bayesian multilayer network model for song similarity, integrating multiple audio features and exogenous data, with comprehensive model comparison and interpretability.

Findings

Stochastic block models best predict song connectivity.

Identified latent communities and hubs across albums and eras.

Covariates like album and temporal proximity influence connectivity.

Abstract

We propose a Bayesian framework for multilayer song similarity networks and apply it to the complete studio discographies of the "Big 4" of thrash metal (Metallica, Slayer, Megadeth, Anthrax). Starting from raw audio, we construct four feature-specific layers (loudness, brightness, tonality, rhythm), augment them with song exogenous information, and represent each layer as a k-nearest neighbor graph. We then fit a family of hierarchical probit models with global and layer-specific baselines, node- and layer-specific sociability effects, dyadic covariates, and alternative forms of latent structure (bilinear, distance-based, and stochastic block communities), comparing increasingly flexible specifications using posterior predictive checks, discrimination and calibration metrics (AUC, Brier score, log-loss), and information criteria (DIC, WAIC). Across all bands, the richest stochastic block specification attains the best predictive performance and posterior predictive fit, while revealing sparse but structured connectivity, interpretable covariate effects (notably album membership and temporal proximity), and latent communities and hubs that cut across albums and eras. Taken together, these results illustrate how Bayesian multilayer network models can help organize high-dimensional audio and text features into coherent, musically meaningful patterns.