Two-Dimensional Structural Characterization of Music Genre Communities in Playlist Co-occurrence Networks

Two-Dimensional Structural Characterization of Music Genre Communities in Playlist Co-occurrence Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Two-Dimensional Structural Characterization of Music Genre Communities in Playlist Co-occurrence Networks Makoto Takeuchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9262383/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Music genre classification shapes how listeners discover music, how platforms design recommendations, and how sociologists study cultural taste. Yet existing genre labels are inconsistent in granularity: they exaggerate boundaries between overlapping categories and hide sociologically important heterogeneity within broad labels. Cultural sociologists have long theorized that genres vary along two independent dimensions, boundary strength and internal differentiation, but existing empirical work has relied on fixed label sets, leaving these dimensions without quantitative operationalization from actual consumption behavior data. Here we propose a two-dimensional framework that extracts music communities bottom-up from playlist co-occurrence networks and characterizes each along two axes: external closure $B(C)$, measuring boundary strength relative to a random null, and internal differentiation $D(C)$, measuring organized internal subdivision. We validate the framework on two independent datasets spanning different platforms, cultural contexts, and time periods, confirming that $B(C)$ and $D(C)$ are statistically independent and that each captures a distinct structural property. The framework reveals genre structures invisible to fixed labels: single labels splitting into communities with different boundary strengths, multiple labels merging into tightly bounded communities, and consumption spheres that no existing label describes. Comparison with prior theoretical predictions is broadly consistent, with the notable exception that Hip-Hop exhibits rich internal differentiation across both datasets, challenging its prevailing single-centered characterization. By providing a label-independent coordinate system grounded in listener behavior, this framework opens a path toward tracking how genre boundaries and internal structures evolve over time, a question that static label systems cannot address. music genre playlist co-occurrence network community detection boundary strength internal differentiation network analysis cultural sociology Full Text Additional Declarations Competing interest reported. The author is employed by the company that operates the AWA music streaming service, from which one of the two datasets used in this study was obtained. There are no patents to declare. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9262383","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631788871,"identity":"851acfb2-3bf7-45b5-ace5-e4e0d3fe15ac","order_by":0,"name":"Makoto Takeuchi","email":"data:image/png;base64,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","orcid":"","institution":"CyberAgent, Inc.","correspondingAuthor":true,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Takeuchi","suffix":""}],"badges":[],"createdAt":"2026-03-30 04:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9262383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9262383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806370,"identity":"b76056b7-9410-4ae5-a387-7bc0b6b0e2ce","added_by":"auto","created_at":"2026-05-08 15:28:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1398949,"visible":true,"origin":"","legend":"","description":"","filename":"submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9262383/v1_covered_c2b4fd6b-d764-4114-aa6e-be263d75f88f.pdf"}],"financialInterests":"Competing interest reported. The author is employed by the company that operates the AWA music streaming service, from which one of the two datasets used in this study was obtained. 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