Using Audio Analysis and Network Structure to Identify Communities in On-Line Social Networks of Artists

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. Evidence suggesting the Myspace artist network structure is closely related to musical genre is presented and a Semantic Web service for accessing this structure is described.