Kurt's Works

Community detection methods from complex network theory are applied to a subset of the Myspace artist network to identify groups of similar artists. Methods based on the greedy optimization of modularity and random walks are used. In a second iteration, inter-artist audio-based similarity scores are used as input to enhance these community detection methods. The resulting community structures are evaluated using a collection of artist-assigned genre tags.

A sample of the Myspace artist network is examined to investigate the relationship between social connectivity and audio-based similarity.  Audio data from the Myspace artist pages is analyzed using well-established signal-based music information retrieval techniques.  In addition to showing that the Myspace artist network exhibits many of the properties common to social networks, we show there is an ambiguous relationship between audio-based similarity and the social network topology.

A sample of the Myspace social network is examined. Using
methods from complex network theory, we show empirically that artists tend to form on-line social connections with artists of the same genre. This motivates the use of on-line social networks as data resources for musicology and music information retrieval.