论文标题
Spotify网络中的受欢迎程度和中心性：特征向量中心的关键过渡
Popularity and Centrality in Spotify Networks: Critical transitions in eigenvector centrality
论文作者
论文摘要
数字音乐访问的现代时代增加了有关音乐消费和创作的数据的可用性，从而促进了对将音乐连接在一起的复杂网络的大规模分析。有关用户流动行为以及音乐协作网络的数据，对于新的数据驱动推荐系统，尤其重要。如果没有彻底的分析，这种协作图可以导致错误或误导性结论。在这里，我们介绍了来自在线音乐流媒体服务Spotify的艺术家的新合作网络，并展示了艺术家的特征向量中心的关键变化，因为删除了低知名艺术家。从古典艺术家到说唱艺术家的中心性的批判性变化表明了网络的更深层次结构特性。提出了一个社会群体中心模型，以模拟这种关键的过渡行为，并观察到主要特征向量之间的切换。该模型对流行性偏见对中心性和重要性的影响的影响进行了新的研究，并为检查网络中的这些缺陷提供了一种新工具。
The modern age of digital music access has increased the availability of data about music consumption and creation, facilitating the large-scale analysis of the complex networks that connect music together. Data about user streaming behaviour, and the musical collaboration networks are particularly important with new data-driven recommendation systems. Without thorough analysis, such collaboration graphs can lead to false or misleading conclusions. Here we present a new collaboration network of artists from the online music streaming service Spotify, and demonstrate a critical change in the eigenvector centrality of artists, as low popularity artists are removed. The critical change in centrality, from classical artists to rap artists, demonstrates deeper structural properties of the network. A Social Group Centrality model is presented to simulate this critical transition behaviour, and switching between dominant eigenvectors is observed. This model presents a novel investigation of the effect of popularity bias on how centrality and importance are measured, and provides a new tool for examining such flaws in networks.
