feat: Influence Chain Explorer — trace how genres and styles propagated through history

[SimplicityGuy]

Visualize how a genre or style propagated across artists, releases, and labels over time — from proto-punk to punk to post-punk to new wave, traced through actual release data.

Concept

Uses Neo4j multi-hop traversal to show the lineage of a genre. "How did punk evolve from proto-punk across labels and cities?" Watch the graph illuminate the chain from The Stooges → The Damned → Joy Division → New Order.

Requires adding INFLUENCED_BY relationships (via MusicBrainz artist-relations — see related issue on MusicBrainz integration).

Proposed Features

1. Genre lineage graph — start from a genre/style, traverse INFLUENCED_BY and DERIVED_FROM edges with a time filter; render as a branching timeline
2. Artist influence chain — shortest influence path between two artists ("Show me how Kraftwerk influenced Daft Punk")
3. Geographic spread overlay — show where in the world the influence spread decade-by-decade (requires country data on releases)
4. "Influence hub" detection — which artists or releases are the most influential bridge points in a genre's history?

Dependencies

• INFLUENCED_BY relationships not yet in the graph — need to be sourced from MusicBrainz artist-relations XML (periodic ingest, match on Discogs IDs)
• DERIVED_FROM style relationships may be inferrable from existing Discogs style hierarchy data

Proposed Endpoints

• GET /api/influence/genre/{style}?before_year=1985 — genre lineage sub-graph
• GET /api/influence/path?from_artist={id}&to_artist={id} — influence chain between two artists
• GET /api/influence/hubs?genre={style}&limit=10 — most influential bridge artists

Implementation Notes

• Traversal via apoc.path.expandConfig with INFLUENCED_BY relationship type and optional year filter
• MusicBrainz ingest: periodic job parsing MusicBrainz XML dumps, matching on shared identifiers (MBID ↔ Discogs ID via lookup table)
• Complements feat(explore): path finder — shortest path between two entities #49 (path finder — shortest any path vs. influence-typed path), feat: Collaboration Network — artist connection graph and centrality #99 (collaboration network), feat: MCP Server — expose the knowledge graph to AI assistants #83 (MCP tools)

Acceptance Criteria

• Genre lineage graph returns for at least 10 well-known genres/styles once MusicBrainz data is ingested
• Influence path endpoint returns typed relationship chain (not just shortest hop)
• Hub detection returns top 10 artists with influence score
• ≥80% test coverage for traversal logic

[SimplicityGuy]

Update: The path finder (#121) and graph discovery/recommender (#82) are now merged, providing the foundation for influence traversal. This issue still requires MusicBrainz data ingestion for INFLUENCED_BY relationships.

[SimplicityGuy]

Status Update — 2026-03-25

Not started. No implementation or design work has been merged to main for this feature.