Chinese Musician Playlist Co-occurrence Network of Power-Law Distribution

Abstract

In this paper, we study the topological structure of Chinese musician playlist co-occurrence network. The metadata of constructed network comes from online music platform. We use processed data to construct a Chinese musician playlist co-occurrence network, in which the node represents a Chinese musician and the edge between nodes represents that two musicians appear together in the same playlist. The playlist collects different song on the same theme, which includes current mood and scene, or preferred song style. In some degree, it plays the role of recording life. For the topological structure of network, we study a variety of statistical properties including node degree distribution, clustering coefficient, average path length and node centrality etc. The resulting network shows that the degree distribution in the Chinese musician playlist co-occurrence network follows the power-law distribution. The network is a scale-free network with an exponent of 3.97, a higher clustering coefficient, a smaller average path length and typical small-world characteristics.