MATCH (bob:Person name: 'Bob')-[:CALLED]->(phone:Phone) MATCH (phone)<-[:USED]-(suspect:Person)-[:VISITED]->(loc:Location address: 'Main St 42') RETURN suspect.name, phone.number Result: "Charlie" , "555-1234" .
Sam partitioned data by case and used for speed. No more JOIN explosions. Epilogue: The Conviction Using Neo4j, the agency linked a money trail, phone calls, and meeting locations across 12 suspects. The prosecutor presented a graph visualization—not as evidence, but as an investigation tool. The jury understood instantly.
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 neo4j in action pdf
MATCH (tip:Tip)-[:MENTIONS]->(person:Person) WHERE tip.timestamp > datetime() - duration('PT5M') RETURN person.name, tip.text Within seconds of a new tip mentioning “Mr. X,” Alex’s dashboard lit up. With 2 million nodes and 5 million relationships, SQL queries took minutes. Neo4j used index-free adjacency —traversing relationships is O(1) per hop. The same queries ran in <50 ms.
“It took 2 milliseconds,” Sam said. “And we didn’t even index anything yet.” Alex needed to know: how is Alice connected to a known criminal, Mr. X? Epilogue: The Conviction Using Neo4j, the agency linked
“We need a faster way to follow relationships,” Alex said.
SQL would need multiple JOINs. In Neo4j: In Neo4j: “Three hops
“Three hops,” Alex whispered. “We can now predict risk chains.” Using collaborative filtering , Sam wrote a query to find people similar to a suspect based on shared locations and contacts: