Understanding the Influence of Genre-Specific Music Using Network Analysis and Machine Learning Algorithms

Abstract

This study analyzes a network of musical influence using machine learning and network analysis techniques. A directed network model is used to represent the influence relations between artists as nodes and edges. Network properties and centrality measures are analyzed to identify influential patterns. In addition, influence within and outside the genre is quantified using in-genre and out-genre weights. Regression analysis is performed to determine the impact of musical attributes on influence. We find that speechiness, acousticness, and valence are the top features of the most influential artists. We also introduce the IRDI, an algorithm that provides an innovative approach to quantify an artist’s influence by capturing the degree of dominance among their followers. This approach underscores influential artists who drive the evolution of music, setting trends and significantly inspiring a new generation of artists. The independent cascade model is further employed to open up the temporal dynamics of influence propagation across the entire musical network, highlighting how initial seeds of influence can contagiously spread through the network. This multidisciplinary approach provides a nuanced understanding of musical influence that refines existing methods and sheds light on influential trends and dynamics.