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Graph Databases: When Relational Models Are Not Enough

Fraud networks, recommendation engines, and knowledge graphs require traversing deep relationships efficiently. Learn when and how to use graph databases.

The Limits of Relational Databases

Relational databases store data in tables and represent relationships via foreign keys and JOINs. For shallow relationships, this works well. But querying highly-connected data (like a social network six degrees deep) requires dozens of expensive JOINs, making it impractical.

Graph Database Use Cases

  • Fraud Detection: Detect fraud rings by finding clusters of accounts sharing phone numbers, devices, or addresses.
  • Recommendation Engines: Suggest products by traversing “customers who bought X also bought Y” relationship chains.
  • Knowledge Graphs: Model complex domain knowledge with rich semantic relationships for AI applications.
# Cypher query to find fraud rings in Neo4jnMATCH (a:Account)-[:SHARES_DEVICE]->(d:Device)<-[:SHARES_DEVICE]-(b:Account)nWHERE a <> bnRETURN a, b, d