What is it about?
This work analyzes the social network of artists by considering their friends, mentors, pupils, and so on. This information to predict whether an artist is influential for another artist. Artists with common connections are more likely to be exposed to each other, which has a high likelihood of resulting in (mutual) artistic influence.
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Why is it important?
Previous research has shown that artistic success is tightly connected to the social network of the artist and its influences. It is hard to obtain information on artistic influence, as it is generally the result of a subjective analysis. Being able to propose a list of possible artistic influences by only considering objective information (the social network of an artist) enables scholars and music enthusiasts to explore artists from a completely different perspective.
Perspectives
Writing this article was a great pleasure as it enables an interesting application that leverages complex technologies to provide a simple but powerful tool for music scholars and enthusiasts.
Nicolas Lazzari
Read the Original
This page is a summary of: Creative influence prediction using graph theory, Intelligenza Artificiale, July 2024, IOS Press,
DOI: 10.3233/ia-240029.
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