A Minimum Spanning Tree Representation of Anime Similarities
Canggih Puspo Wibowo

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Analysis and Summarization · Music and Audio Processing · Human Motion and Animation
