A Minimum Spanning Tree Representation of Anime Similarities

TL;DR

This paper introduces a novel method for representing anime similarities using a minimum spanning tree based on crew, score histogram, and topic similarities, highlighting significant titles through centrality measures.

Contribution

The work presents a new graph-based approach to model anime similarities and identifies key anime using centrality metrics, which was not previously explored.

Findings

Minimum spanning tree effectively models anime similarities.

Centrality measures identify significant anime.

The method reveals relationships between anime titles.

Abstract

In this work, a new way to represent Japanese animation (anime) is presented. We applied a minimum spanning tree to show the relation between anime. The distance between anime is calculated through three similarity measurements, namely crew, score histogram, and topic similarities. Finally, the centralities are also computed to reveal the most significance anime. The result shows that the minimum spanning tree can be used to determine the similarity anime. Furthermore, by using centralities calculation, we found some anime that are significant to others.