Neo4j

When to Use

• Knowledge Graphs: Connecting silos of data (Users, Docs, Concepts).
• Fraud Detection: Finding "Rings" of users sharing IDs/Devices.
• Recommendation Engines: "People who bought X also bought Y" (Collaborative Filtering).
• GraphRAG (2025): Enhancing LLM context by traversing a knowledge graph instead of just vector similarity.

Quick Start (Cypher)

-- Create nodes and relationships
CREATE (alice:Person {name: 'Alice'})
CREATE (bob:Person {name: 'Bob'})
CREATE (alice)-[:KNOWS {since: 2024}]->(bob);

-- Query friends of friends
MATCH (p:Person {name: 'Alice'})-[:KNOWS]->(friend)-[:KNOWS]->(fof)
RETURN fof.name;

Core Concepts

Cypher

(node)-[RELATIONSHIP]->(node)

Index-Free Adjacency

Graph Data Science (GDS)

Best Practices (2025)

• Use GraphRAG: Combine Vector Search (Semantic) with Graph Traversal (Structural) for better AI answers.
• Model Relationships Richly: Put properties on relationships (e.g.,

  [:REVIEWED {rating: 5, date: ...}]

  ).
• Use Labels: Always label nodes (

  :Person

  ) to optimize scans.

• Don't create Supernodes: A node with 1 Million edges (e.g.,

  (Justin Bieber)-[:FOLLOWED_BY]->(...)

  ) hurts traversal performance.
• Don't use it for simple lookups: If you just need

  ByKey

  or

  Range

  , use a K-V store.

References

• Neo4j Documentation