Creating a network of one's taste in music

Note aux lecteurs francophones

Merci pour l'intérêt que vous portez à mon travail. Cet article a initialement été rédigé en langue anglaise, et je n'ai pas le projet de le traduire pour l'instant. Notez que le reste de mon site est généralement écrit en Français.

introduction

As most music nerds do, I've always taken pride in the versatility of my music taste. My playlists continuously jump from post-avant-jazzcore, to progressive dreamfunk, to basically every music genre. From 2017 and on, I've listened to a new album everyday, while continuously discovering new genres and musical innovations. Today: I present you a network of my music taste:

Methodology

I'm thankful I've been using last.fm every day since 2012. Lastfm allows me to keep track to every single song I listen to, and overall gives insights about music habits, and various artists and albums recommandations. I recommend to everyone to give this website a go. I was able to download and convert my data to css using @benfoxall's free tool Last.fm to csv. I then filtered my top 500 most listened artists by using the magic of Excel's pivot tables.

The networking part was made using Hyphe crawling the "similar artists" section of each 500 artists. I then edited the graph using Gephi and pimped it using a modularity algorithm. Each modularity class corresponds to a music genre.