Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks

TL;DR

This work introduces a network-based framework to quantify creativity and influence within Ukiyo-e’s master-disciple tradition over about a century. By building Image Similarity-Based (ISN) and Style-Based (SSN) networks from a fine-tuned VGG-derived embedding and projecting them to artist-level relations, the study computes Betweenness Centrality and the Disruption Index $D5$ to trace how visual features and stylistic innovations propagate. It finds that overall disruptiveness declines as Ukiyo-e matures, while within-school disruption remains relatively high, and identifies pivotal artists (e.g., Toyokuni I/III, Kunisada I) who bridge styles or introduce novel directions. The results offer a quantitative complement to art-historical analysis, revealing how external factors like Meiji-era changes intersect with traditional transmission, and demonstrating a scalable approach for decoding artistic influence in Eastern art forms.

Abstract

Artwork research has long relied on human sensibility and subjective judgment, but recent developments in machine learning have enabled the quantitative assessment of features that humans could not discover. In Western paintings, comprehensive analyses have been conducted from various perspectives in conjunction with large databases, but such extensive analysis has not been sufficiently conducted for Eastern paintings. Then, we focus on Ukiyo-e, a traditional Japanese art form, as a case study of Eastern paintings, and conduct a quantitative analysis of creativity in works of art using 11,000 high-resolution images. This involves using the concept of calculating creativity from networks to analyze both the creativity of the artwork and that of the artists. As a result, In terms of Ukiyo-e as a whole, it was found that the creativity of its appearance has declined with the maturation of culture, but in terms of style, it has become more segmented with the maturation of culture and has maintained a high level of creativity. This not only provides new insights into the study of Ukiyo-e but also shows how Ukiyo-e has evolved within the ongoing cultural history, playing a culturally significant role in the analysis of Eastern art.

Figures (12)

• Figure 1: Conceptual Diagram of the Model Used
• Figure 2: Ukiyo-e Faces Dataset
• Figure 3: The active era of Ukiyo-e artists.
• Figure 4: Constructing Style-Based Network (SSN)
• Figure 5: Style-Based Clusters