Heavy Metal is a popular sub culture, and in itself is highly tribalized, which makes it an interesting domain to research how cultures and sub cultures relate and evolve. To study this, we scrape... Show moreHeavy Metal is a popular sub culture, and in itself is highly tribalized, which makes it an interesting domain to research how cultures and sub cultures relate and evolve. To study this, we scrape the Encyclopaedia Metallum heavy metal music archive website to generate a large scale networked data set. Bands are linked through shared musicians, and each band can be labelled with multiple user contributed genres. By applying Word2Vec on genre co-occurences, and hierarchical network clustering on the band collaboration graph, we gain insight into how music genres relate to each other. While the Word2Vec results show some interesting patterns with regards to the observed clusters, the hierarchical clustering proves to be more inconclusive, partially caused by factors beyond genre that generate the network. From a machine learning point of view, this case is an instance of the more general problem of understanding label structure in networked data. Show less
