Community Detection with Metadata in a Network of Biographies of Western Art Painters

TL;DR

This paper explores the influence network of Western art painters using Wikipedia biographies, employing metadata-enhanced community detection and modified centrality measures to analyze artistic impact and community structure.

Contribution

It introduces a modified modularity maximization method incorporating metadata for community detection in art influence networks.

Findings

Communities align with artistic genres and schools.

Influential artists impact beyond their communities.

Modified centrality measures reveal key influencers.

Abstract

In this work we look at the structure of the influences between Western art painters as revealed by their biographies on Wikipedia. We use a modified version of modularity maximisation with metadata to detect a partition of artists into communities based on their artistic genre and school in which they belong. We then use this community structure to discuss how influential artists reached beyond their own communities and had a lasting impact on others, by proposing modifications on standard centrality measures.