Networks of Music Groups as Success Predictors

TL;DR

This study constructs a network of music groups based on shared performers to predict their success, demonstrating that network measures can serve as effective indicators and are applicable to other collaborative arts fields.

Contribution

Introduces a network-based approach to predict music group success using shared performers, applicable to other team-based creative collaborations.

Findings

Network measures predict group success with reasonable accuracy.

Major network metrics correlate with long-term success.

Method is transferable to other arts and humanities collaborations.

Abstract

More than 4,600 non-academic music groups emerged in the USSR and post-Soviet independent nations in 1960--2015, performing in 275 genres. Some of the groups became legends and survived for decades, while others vanished and are known now only to select music history scholars. We built a network of the groups based on sharing at least one performer. We discovered that major network measures serve as reasonably accurate predictors of the groups' success. The proposed network-based success exploration and prediction methods are transferable to other areas of arts and humanities that have medium- or long-term team-based collaborations.