# Quantifying Creativity in Art Networks

arXiv: https://arxiv.org/abs/1506.00711 · HTML: https://mlsanity.com/paper/1506.00711/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2015.

## Built on by (most cited first, 13 of 13)

- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models](https://mlsanity.com/paper/2501.15147/) (2025)
- [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022)
- [DeepCreativity: Measuring Creativity with Deep Learning Techniques](https://mlsanity.com/paper/2201.06118/) (2022)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Challenges in creative generative models for music: a divergence maximization perspective](https://mlsanity.com/paper/2211.08856/) (2022)
- [Docent: A content-based recommendation system to discover contemporary art](https://mlsanity.com/paper/2207.05648/) (2022)
- [Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution](https://mlsanity.com/paper/2503.13531/) (2025)
- [Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane](https://mlsanity.com/paper/2408.10356/) (2024)
- [Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks](https://mlsanity.com/paper/2505.08284/) (2025)
- [M-ArtAgent: Evidence-Based Multimodal Agent for Implicit Art Influence Discovery](https://mlsanity.com/paper/2604.07468/) (2026)
- [Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting](https://mlsanity.com/paper/2605.19554/) (2026)
- [Towards Defining, Measuring, and Leveraging Creativity in AI Applications](https://mlsanity.com/paper/2610.12845/) (2026)

## Abstract (arXiv)

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".