P-003 · 2025 · Team project
Artist influence networks with Neo4j
A team project analysing musical influence through cover-song relationships, graph centrality and artist communities.
Contribution and context
I contributed to a graph-database solution for analysing artist influence and musical communities. The project represents cover-song relationships from SecondHandSongs as a directed, weighted network spanning 1960–2020.
Method
Cypher and Neo4j Graph Data Science queries calculate in-degree, PageRank and betweenness. Clustering groups artists by their centrality profiles, while Neo4j Bloom supports exploration of influential artists and their connections.
Public outputs
The repository provides graph data, Cypher queries and visualisation instructions. Its documented analysis identifies The Beatles as the leading artist across the selected influence measures, with other prominent artists including Bob Dylan and Elvis Presley.
Scope
Influence is defined through recorded cover-song relationships. The results describe this dataset and graph construction; they are not a universal ranking of musical importance.