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@@ -45,7 +45,7 @@ The data collection process was inspired by [DISCO-10M](https://arxiv.org/abs/23
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  Starting from an initial seed list of artists, we can discover new artists by recursively exploring the artists listed in the "Fans might also like" section.
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  We explore the related artists graph for as long as we are able to find new artists.
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- For a given artist, we can extract some metadata about them, such as their name and number of subscribers, as well as a list of all of their songs and music videos.
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  Importantly, each song or music video is associated with a YouTube URL (obtained from its ID). The collected metadata fields are as follows: song_id, title, artist_names, artist_ids, album_name, album_id, isExplicit, views, duration.
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  DISCO-10M used a seed list of 18 artists, chosen to represent a variety of genres. However, we found that this is not sufficient for exploring the artist graph of YouTube Music. Starting from this seed list, we were able to discover only 90,007 artists and 5,399,389 songs.
 
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  Starting from an initial seed list of artists, we can discover new artists by recursively exploring the artists listed in the "Fans might also like" section.
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  We explore the related artists graph for as long as we are able to find new artists.
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+ For a given artist, we can extract their metadata, such as their name and number of subscribers, as well as a list of all of their songs and music videos.
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  Importantly, each song or music video is associated with a YouTube URL (obtained from its ID). The collected metadata fields are as follows: song_id, title, artist_names, artist_ids, album_name, album_id, isExplicit, views, duration.
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  DISCO-10M used a seed list of 18 artists, chosen to represent a variety of genres. However, we found that this is not sufficient for exploring the artist graph of YouTube Music. Starting from this seed list, we were able to discover only 90,007 artists and 5,399,389 songs.