Quantifying Creativity in Art Networks

TL;DR

This paper introduces a computational network-based framework to quantify creativity in art, assessing originality and influence of artworks like paintings and sculptures through network centrality measures.

Contribution

It presents a novel network construction method for evaluating creativity, linking originality and influence, and introduces a validation methodology called the 'time machine experiment.'

Findings

Framework applied to over 62,000 artworks.

Network centrality correlates with perceived creativity.

Validation method supports the framework's effectiveness.

Abstract

Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the "time machine experiment".