MATCH (p:Person name: 'Charlie')-[:VISITED|KNOWS]->(common)<-[:VISITED|KNOWS]-(other:Person) WHERE p <> other RETURN other.name, count(common) AS similarity ORDER BY similarity DESC This returned unknown associates—perfect for expanding investigations. The agency integrated Neo4j with Kafka. Every new tip became a new relationship. A trigger query ran every minute:
CREATE (alice:Person name: 'Alice', age: 34) CREATE (bob:Person name: 'Bob', age: 29) CREATE (alice)-[:KNOWS]->(bob) A witness said: “Bob called a phone number, and that phone was used near the crime scene.” neo4j in action pdf
MATCH path = shortestPath( (alice:Person name: 'Alice')-[:KNOWS*..5]-(mrX:Person name: 'Mr. X') ) RETURN path The result: Alice → KNOWS → Bob → KNOWS → Dave → KNOWS → Mr. X MATCH (p:Person name: 'Charlie')-[:VISITED|KNOWS]->
SQL would need multiple JOINs. In Neo4j: -[:VISITED|KNOWS]-(other:Person) WHERE p <
“We need a faster way to follow relationships,” Alex said.