Lyrical Influence Networks Dataset (LIND)

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Abstract/Contents

Abstract: This dataset contains three directed networks (graphs) that encode similarity between song genres, artists, and writers based on phrases shared across song lyrics over time. Each graph is presented in a separate .graphml file. The graphs contain nodes for 214 genres, 42,802 artists, and 95,349 writers respectively, with shared phrase relationships constructed from the intact lyrics of 554,206 songs. Information regarding the construction of the networks, as well as example analyses, are provided in the accompanying published ISMIR paper (Atherton & Kaneshiro, 2016). The intact lyrics used for construction of the networks were obtained from LyricFind (http://www.lyricfind.com/) via signed research agreement; the networks are published here with permission from LyricFind. Graph files are available for download in compressed (.zip) format and range in size from 342 KB - 776.5 MB compressed to 2.7 MB - 9.04 GB uncompressed.

Type of resource	software, multimedia
Date created	2016

Creator	Atherton, Jack
Contributing author	Kaneshiro, Blair

Subject	Center for Computer Research in Music and Acoustics
Subject	Department of Music
Subject	Networks
Subject	Lyrics
Genre	Dataset

Related Publication	Jack Atherton and Blair Kaneshiro (2016). I Said It First: Topological Analysis of Lyrical Influence Networks. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR).
Related item	Title ISMIR paper (Atherton & Kaneshiro, 2016)
Location	https://purl.stanford.edu/zy061bp9773

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License: This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).

Preferred Citation: Jack Atherton and Blair Kaneshiro (2016). Lyrical Influence Networks Dataset (LIND). Stanford Digital Repository. Available at: http://purl.stanford.edu/zy061bp9773

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