We study the topology of several music recommendation networks, which arise from relationships between artist, co-occurrence of songs in play lists or experts’ recommendation. The analysis uncovers the emergence of complex network phenomena in these kinds of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perception.
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The value of , which is the highest degree in the network, can be estimated in scale-free networks as .
Note that in this case we refer to degree distribution instead of cumulative degree distribution . Both power-law exponents are related by the expression .
