{
  "id": "stack-part:graph-database",
  "_kind": "StackPart",
  "_file": "domain/stack-parts/graph-database.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Graph Database",
    "category": "data-store",
    "description": "Database purpose-built for storing and traversing graph structures —\nnodes (entities) and edges (relationships) with properties on both.\nGraph databases excel at multi-hop relationship queries that would\nrequire expensive recursive joins in a relational database.\n\nCommon use cases: fraud detection, recommendation engines, knowledge\ngraphs, identity and access management, and social networks. Neo4j\nuses the Cypher query language; Amazon Neptune supports Gremlin and\nSPARQL; Dgraph uses GraphQL+DQL. Property graphs (Neo4j, Neptune) and\nRDF triplestores (Stardog, Amazon Neptune/SPARQL) are the two dominant\nparadigms. Vector + graph hybrids are emerging for AI knowledge graph\napplications.\n"
  },
  "outgoingEdges": [
    {
      "from": "stack-part:graph-database",
      "to": "tool:neo4j",
      "kind": "implemented_by",
      "attributes": {}
    }
  ],
  "incomingEdges": [
    {
      "from": "tool:neo4j",
      "to": "stack-part:graph-database",
      "kind": "implements_stack_part",
      "attributes": {}
    },
    {
      "from": "tool-server:mcp-neo4j",
      "to": "stack-part:graph-database",
      "kind": "integrates_with",
      "attributes": {}
    }
  ]
}