Creative influence prediction using graph theory

Abstract

Creative influence is responsible for a considerable part of the creative process of an artist and can largely be associated with their social circle. It has been observed that the type and amount of relationships with other fellow artists correlates with the success of an artist. Most of the recent literature has focused on using artefact similarity as a proxy for creative influence between two artists. However, this approach neglects the significance of an artist’s social network. In this work, we rely on an ontology that comprehensively model the relationship between individuals as a Knowledge Graph and we design an explainable method based on graph theory to predict the influences of an artist given their social network. We evaluate our method on a dataset of relationships between Jazz musicians and achieve accurate results when compared to baselines that rely on the distribution of the data. Our results are aligned with relevant works from the socio-cognitive and psychology fields. We show that our method generalises to resources where information on influence is not directly available and can be used to enrich existing Knowledge Graphs. The code and the ontology developed is shared at https://github.com/n28div/influence_prediction under CC-BY license.