Duke Listens!: Visit my main blog at MusicMachinery.com

Update: There's a super-zoomable version on the seadragon gallery page. I can't figure out how to directly link to the image ... It is the 5th one from the right. The plot is created by crawling the similar artist graph of Last.fm (starting with Nightwish of all places) using the last.fm audioscrobbler webservices. The artists are arranged on the graph using a DrL graph layout algorithm (DrL is a force-directed layout algorithm that works with very large data sets. More info about this algorithm can be found in this paper ). The nodes in the graph are colored based upon the most frequent tags, while the edges are colored based upon their 'betweeness centrality score'. The area of a node in the graph is approximately proportional to the popularity of the artist.

Nepusz has also created an interactive map that allows you to type in the name of a few artists to see where they live on the map - or you can just enter your last.fm user name and it will show you where all your favorite artists are in the world of music.

The layout algorithm does a pretty good job of showing the large scale structure of the artist space. Artists with similar genre tags are well clustered. The ability to see where a particular artist is located on the map is very nice and the last.fm user integration is particularly sweet. Interesting too, is how the popular artists seem to be clustered in the graph. The larger vertices in the red-rock area form a very tight line. This may be an effect of using the Last.fm artist similarity which has a popularity bias (the top 10 artists similar to the beatles are all very popular artists).

What I really wish I could do is to use these plots for music discovery - I'd like to be able to mouse over a vertex to see what the band is (and even be able to listen to the band). It would be really interesting, for instance, to explore the point where the electronic and the rock world meets (like in the subsection of the graph shown here on the left - what artist is represented by the large orange node?). It'd be interested to see what the outliers are (I wonder what this reggae/ska artist is doing near the jazz, as seen in the subgraph on the right). I'd like to be able to zoom in and see some of the finer structure (if I zoom in on the Nightwish neighborhood, do I find more finnish, gothic metal?).

I'm a sucker for such visualizations, I think they can be a powerful tool for helping people to understand and explore a music space, and they can reveal relations and structure that are not evident in simple lists of similar artists. But creating these visualizations are not easy. Without special care they can easily turn into meaningless blobs. Nepusz has done an excellent job finding the right embedding algorithm, color and sizing strategy for the data. I hope he continues to add interactivity to his plots. Well done.