Predicting musician success from early career collaboration structures

This work investigates whether early-career collaboration structures among musicians can predict long-term artistic success. Using MusicBrainz and Spotify data, we construct a large-scale collaboration network and model the generative processes underlying tie formation using Exponential Random Graph Models (ERGMs). We show that structural tendencies such as triadic closure, homophily, and productivity-driven exposure shape early collaboration, while weak ties are underrepresented relative to structural expectations. Using these insights, we engineer interpretable features and learned graph embeddings to predict long-term success, measured by Spotify follower count. Our findings demonstrate that early-network connectivity—particularly weak ties, cross-community bridging, and deviations from expected structural patterns—adds predictive signal beyond metadata baselines.