Papers › Analysis of a Spotify Collaboration Network for Small-World Properties
Analysis of a Spotify Collaboration Network for Small-World Properties
Raquel Ana Magalhães Bush
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
This paper examines the small-world properties of a Spotify artist feature collaboration network, focusing on clustering and diameter. We analyze the giant component and subgraphs based on genres, country-specific charts, and detected communities to assess their small-world characteristics. Results indicate that the network is scale-free and follows a power-law degree distribution, with highly popular artists serving as central hubs. Louvain community detection reveals distinct collaboration clusters aligned with genre-based and industry-driven connections. These findings offer insights into music recommendation systems and digital collaboration trends, contributing to a broader understanding of artist networks in the digital age.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections