Neo4j Workshop

gistfile1.txt

Demo Server : https://github.com/neo4j-graph-examples/recommendations

https://github.com/neo4j-graph-examples/recommendations/tree/main/data

The database is also available on https://demo.neo4jlabs.com:7473
Username "recommendations", password: "recommendations", database: "recommendations"


https://docs.google.com/document/d/1OLr3KnYpQpTRBJPKvSoY4ikmg5sP5Odwh-E6piuKQA0/edit?usp=sharing




SIMPLE MODEL


CREATE CONSTRAINT person_id_idx FOR (n:Person) REQUIRE n.id IS UNIQUE ;
CREATE CONSTRAINT address_id_idx FOR (n:Address) REQUIRE n.id IS UNIQUE ; 
CREATE INDEX person_name_idx FOR (n:Person) ON n.name ;

DATA

CREATE (p:Person {id:1, name:'John Doe', gender:'Male'})
CREATE (a1:Address {id:1, line1:'1 first ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a2:Address {id:2, line1:'13 second ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a3:Address {id:3, line1:'13 third ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a4:Address {id:4, line1:'1 fourth ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a5:Address {id:5, line1:'5 other ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (p)-[:HAS_ADDRESS {start:'2001-01-01', end:'2003-12-31'}]->(a1)
CREATE (p)-[:HAS_ADDRESS {start:'2004-01-01', end:'2008-12-31'}]->(a2)
CREATE (p)-[:HAS_ADDRESS {start:'2009-01-01', end:'2015-12-31'}]->(a3)
CREATE (p)-[:HAS_ADDRESS {start:'2016-01-01', end:'2020-12-31'}]->(a4)
CREATE (p)-[:HAS_ADDRESS {start:'2021-01-01'}]->(a5)


Query 1 – Get the latest address

MATCH (p:Person {name:'John Doe'})-[r:HAS_ADDRESS]->(a)
WHERE r.end is null
RETURN a


Query 2 – Get the first address

MATCH (p:Person {name:'John Doe'})-[r:HAS_ADDRESS]->(a)
WITH r, a
ORDER BY r.start ASC
WITH r,a
RETURN a
LIMIT 1

Query 3 – Get the third address

MATCH (p:Person {name:'John Doe'})-[r:HAS_ADDRESS]->(a)
WITH r, a
ORDER BY r.start ASC
WITH r,a
RETURN a
SKIP 2
LIMIT 1




ADVANCE MODEL

CREATE CONSTRAINT person_id_idx FOR (n:Person) REQUIRE n.id IS UNIQUE ;
CREATE CONSTRAINT address_id_idx FOR (n:Address) REQUIRE n.id IS UNIQUE ; 
CREATE INDEX person_name_idx FOR (n:Person) ON n.name ;

DATA

CREATE (p:Person {id:1, name:'John Doe', gender:'Male'})
CREATE (a1:Address {id:1, line1:'1 first ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a2:Address {id:2, line1:'13 second ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a3:Address {id:3, line1:'13 third ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a4:Address {id:4, line1:'1 fourth ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (a5:Address {id:5, line1:'5 other ln', city:'Edison', state:'NJ', zip:'11111'})
CREATE (p)-[:FIRST]->(r1:Rental {start:'2001-01-01', end:'2003-12-31'})-[:HAS_ADDRESS]->(a1)
CREATE (r1)-[:NEXT]->(r2:Rental {start:'2004-01-01', end:'2008-12-31'})-[:HAS_ADDRESS]->(a2)
CREATE (r2)-[:NEXT]->(r3:Rental {start:'2009-01-01', end:'2015-12-31'})-[:HAS_ADDRESS]->(a3)
CREATE (r3)-[:NEXT]->(r4:Rental {start:'2016-01-01', end:'2020-12-31'})-[:HAS_ADDRESS]->(a4)
CREATE (r4)-[:NEXT]->(r5:Rental {start:'2021-01-01'})-[:HAS_ADDRESS]->(a5)
CREATE (p)-[:LATEST]->(r5)


Query 1 – Get the latest address

MATCH (p:Person {name:'John Doe'})-[:LATEST]->()-[:HAS_ADDRESS]->(a)
RETURN a

Query 2 – Get the first addressZ

MATCH (p:Person {name:'John Doe'})-[:FIRST]->()-[:HAS_ADDRESS]->(a)
RETURN a

Query 3 – Get the third address

MATCH (p:Person {name:'John Doe'})-[:FIRST]->()-[:NEXT*2..2]->()-[:HAS_ADDRESS]->(a)
RETURN a




1. BASIC - Find all movies
--------------------------
MATCH (m:Movie)
RETURN m.title, m.released
ORDER BY m.released

Returns all movies with their titles and release years, ordered chronologically.

================================================================

2. BASIC - Find a specific actor and their movies
--------------------------------------------------
MATCH (actor:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie:Movie)
RETURN actor.name, movie.title, movie.released
ORDER BY movie.released

Finds all movies that Tom Hanks acted in.

================================================================

3. BASIC - Find co-actors of a specific actor
----------------------------------------------
MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie:Movie)<-[:ACTED_IN]-(coActor:Person)
RETURN DISTINCT coActor.name
ORDER BY coActor.name

Finds all actors who have worked with Tom Hanks in any movie.

================================================================

4. INTERMEDIATE - Movies with their cast and crew counts
--------------------------------------------------------
MATCH (m:Movie)
OPTIONAL MATCH (m)<-[:ACTED_IN]-(actor:Person)
OPTIONAL MATCH (m)<-[:DIRECTED]-(director:Person)
OPTIONAL MATCH (m)<-[:PRODUCED]-(producer:Person)
WITH m, count(DISTINCT actor) AS actors, count(DISTINCT director) AS directors, count(DISTINCT producer) AS producers
RETURN m.title, m.released, actors, directors, producers
ORDER BY actors DESC

Returns movies with counts of actors, directors, and producers.

================================================================


5. INTERMEDIATE - Most prolific actors (by movie count)
--------------------------------------------------------
MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)
WITH actor, count(movie) AS movieCount
WHERE movieCount >= 3
RETURN actor.name, movieCount
ORDER BY movieCount DESC, actor.name

Finds actors who appeared in 3 or more movies, ordered by movie count.

================================================================

6. ADVANCED - Shortest path between two actors
-----------------------------------------------
MATCH path = shortestPath((actor1:Person {name: "Kevin Bacon"})-[*]-(actor2:Person {name: "Tom Hanks"}))
WHERE actor1 <> actor2
RETURN path, length(path) AS pathLength

Finds the shortest connection between Kevin Bacon and Tom Hanks (classic "Six Degrees of Kevin Bacon").

================================================================

7. ADVANCED - Movie recommendations based on shared actors
-----------------------------------------------------------
MATCH (user:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie:Movie)<-[:ACTED_IN]-(coActor:Person)
MATCH (coActor)-[:ACTED_IN]->(recommendation:Movie)
WHERE NOT (user)-[:ACTED_IN]->(recommendation)
WITH recommendation, count(DISTINCT coActor) AS sharedActors
RETURN recommendation.title, recommendation.released, sharedActors
ORDER BY sharedActors DESC, recommendation.title
LIMIT 10

Recommends movies based on actors who worked with Tom Hanks, ranked by number of shared connections.

================================================================

8. ADVANCED - Complex aggregation with multiple relationship types
-------------------------------------------------------------------
MATCH (m:Movie)
OPTIONAL MATCH (m)<-[:ACTED_IN]-(actor:Person)
OPTIONAL MATCH (m)<-[:DIRECTED]-(director:Person)
OPTIONAL MATCH (m)<-[:PRODUCED]-(producer:Person)
OPTIONAL MATCH (m)<-[:WROTE]-(writer:Person)
WITH m, 
     collect(DISTINCT actor.name) AS actors,
     collect(DISTINCT director.name) AS directors,
     collect(DISTINCT producer.name) AS producers,
     collect(DISTINCT writer.name) AS writers
RETURN m.title, 
       m.released,
       size(actors) AS actorCount,
       size(directors) AS directorCount,
       size(producers) AS producerCount,
       size(writers) AS writerCount,
       actors[0..3] AS topActors
ORDER BY actorCount DESC

Comprehensive movie analysis with all crew types and partial cast display.

================================================================

9. EXPERT - Network analysis: Find highly connected movie clusters
--------------------------------------------------------------------
MATCH (m1:Movie)<-[:ACTED_IN]-(actor:Person)-[:ACTED_IN]->(m2:Movie)
WHERE id(m1) < id(m2)
WITH m1, m2, count(actor) AS sharedActors
WHERE sharedActors >= 2
RETURN m1.title AS movie1, 
       m1.released AS year1,
       m2.title AS movie2, 
       m2.released AS year2,
       sharedActors,
       abs(toInteger(m1.released) - toInteger(m2.released)) AS yearDiff
ORDER BY sharedActors DESC, yearDiff ASC
LIMIT 15

Finds movie pairs with significant actor overlap and calculates the time difference between releases.

================================================================