An Analysis Of On-Line Music Artist Networks

Abstract:

We are interested in using online social networks to automatically determine relationships between musicians and artists. We hope to leverage such information for computational musicology studies and for designing new music recommendation systems.
Myspace has become the de-facto standard for web-based music artist promotion. It is estimated there are around 7 million artist pages on Myspace. These pages typically include some media (streaming audio) and a list of “friends” specifying social connections. This combination of user-authored media and a user-specified social network provides a unique data set that is unprecedented in scope and scale.
We sample a portion of the Myspace artist network – only including artist pages in our sample (pages that include user-authored audio files). We also collect audio data from these pages. We show this network conforms in many respects to the topologies expected in social networks. A variation on the concept of assortativity is used to examine the network structure in the context of musical genre. Community structure is also evaluated with respect to musical genre. Finally, audio-based analysis is used to as a means of agglomerative community detection.
The network statistics for our sample are summarized in Table 1.

#nodes
#edges
ave degree
ave shrt pth
diameter
clstr coeff
undirected
15478
91326
11.80
4.47
9
.219
directed
15478
120487
15.57
6.42
16
-
Similar values are commonly reported in social networks [Costa 2007]. However our sample size is insufficient to assume these values are indicative of the entire network.
The cumulative degree distributions for the network sample suggest close to a scale-free topology. However, the power-law fit breaks down for high and low values of degree. Similar “broad-scale” degree distributions have been reported for citation networks and movie actor networks [Amaral 2000].

Assortativity
We use the concept of genre to evaluate how the network structure relates to music. Generally, an artist or a song is associated with one or more musical genres (i.e. rock, pop, rap, etc.). On Myspace, the artist-user is given the option to specify a genre association. The result is each artist page is associated with between 0 and 3 genre labels selected from a static set of 119 genre labels. We are interested in the network assortativity with respect to genre – if there is a high degree of assortative mixing with respect to genre, this suggests the network structure could be meaningful in the context of musicology and music recommendation. However, we are confronted with the unique problem of multiple labels – each node is associated with between 0 and 3 node types. We propose two minor modifications to the assortativity calculation proposed by [Newman 2003]. In one method, we simply truncate the list of genre labels so each node is only associated with one label. This results in a value of r=0.35. In the second method, we preserve all genre labels and consider two nodes to be of the same type if they share one or more genre labels. This results in a value of r=0.78. Both methods suggest some level of assortative mixing, although on nearly opposite ends of the spectrum. It is also shown that the number of shared genre labels between artist pairs decreases as geodesic distance between artists increases. These results suggest that the structure of this musician network is meaningful in the context of music-related studies.

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