Novelty and influence of creative works, and quantifying patterns of advances based on probabilistic references networks

Abstract Recent advances in the quantitative, computational methodology for the modeling and analysis of heterogeneous large-scale data are leading to new opportunities for understanding human behaviors and faculties, including creativity that drives creative enterprises such as science. While innovation is crucial for novel and influential achievements, quantifying these qualities in creative works remains a challenge. Here we present an information-theoretic framework for computing the novelty and influence of creative works based on their generation probabilities reflecting the degree of uniqueness of their elements in comparison with other works. Applying the formalism to a high-quality, large-scale data set of classical piano compositions–works of significant scientific and intellectual value–spanning several centuries of musical history, represented as symbolic progressions of chords, we find that the enterprise’s developmental history can be characterised as a dynamic process composed of the emergence of dominant, paradigmatic creative styles that define distinct historical periods. These findings can offer a new understanding of the evolution of creative enterprises based on principled measures of novelty and influence.