MuzLink: Connected beeswarm timelines for visual analysis of musical adaptations and artist relationships

Author:

Lévesque François1,Hurtut Thomas1ORCID

Affiliation:

1. Polytechnique Montréal, Montreal, QC, Canada

Abstract

The rise of open data in the cultural domain is democratizing access to complex datasets usually presented as large multivariate and multilayered graphs. However, the exploration of such datasets is challenging for laypersons. The objective of this work is to develop and evaluate a new method for exploring and understanding a specific type of multilayered graph that combines a large bipartite graph with a set of tree structures. This paper proposes MuzLink, an interactive visualization tool that allows the user to navigate, search, locate, and compare collaborative and influential relationships between musical artists through the exploration of musical adaptations. The proposed tool is based on a set of connected timelines visualizing how an artist’s collaborations, inspirations, and influences evolved over time. This design study is conducted in close collaboration with BAnQ, the national library and archives agency of the Quebec government. A controlled user study, done with a group of BAnQ users, and two case studies, show how the proposed approach is capable of performing a considerable set of analytical and exploratory tasks.

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

SAGE Publications

Subject

Computer Vision and Pattern Recognition

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fostering Collaboration in Science: Designing an Exploratory Time Travel Visualization;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

2. MFCSNet: A Musician–Follower Complex Social Network for Measuring Musical Influence;Entertainment Computing;2024-01

3. A visualization technique to assist in the comparison of large meteorological datasets;Computers & Graphics;2022-05

4. Explorative Visual Analysis of Rap Music;Information;2021-12-28

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