Modeling Music Streaming as Bipartite Network

Complex network theory and visualization are two essential tools to study real-world systems and internet applications. Using them, we investigate traffic patterns in the Spotify network through a specialized analysis grounded in bipartite graph theory. Leveraging data from the University of Calgary, the study explores the dynamics of client-server interactions in the context of music streaming. The client-server model’s adaptability on modern platforms like Spotify is emphasized, highlighting the bipartite graph model’s relevance in revealing connectivity patterns between clients and servers. The study based on centrality measures and its distributions unveil structural characteristics, offering insights into connectivity patterns and the importance of connections. Using a Spotify network dataset, we provide a comprehensive overview of these measures, including observations on distributions, and layered network representations.