The presentation "Introduction to Neo4j" was given at the Cloud Study Network community by Andreas Kollegger on September 23, 2021. Event details can be found at https://www.meetup.com/Cloud-Study-Network/events/280683331/ Recording of the call can be accessed at https://www.youtube.com/watch?v=4QFJd4pkV7U

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Introduction to Neo4j
Andreas Kollegger
abk@neo4j
Neo4j Graph Academy Live

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we will answer these questions:
• What is a graph database?
• Why use a graph database?
• How do you query a graph database?
• How is this transformative?
In this session

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What is a graph database?
graph + database = graph database

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A graph is...
a mathematical structure used to model
pairwise relations between objects
Seven Bridges of Konigsberg problem. Leonhard Euler, 1735

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A graph database is...
a database which organizes data using graph structures
a graph

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Bridges:
1. Krämerbrücke (Trader's Bridge)
2. Schmiedebrücke (Forged Bridge)
3. Holzbrücke (Wood Bridge)
4. Dom Brücke (Cathedral Bridge)
5. Grüne Brücke (Green Bridge)
6. Köttelbrücke (Dung Bridge)
7. Hohe Brücke (High Bridge)
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A graph database is...
a database which organizes data using graph structures
Districts:
A. Kneiphof (Island, West)
B. Altstadt (North)
C. Lomse (Island, East)
D. Löbenicht (South)
a graph with data

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Bridges:
1. Krämerbrücke (Trader's Bridge)
cost: 1.0
2. Schmiedebrücke (Forged Bridge)
cost: 1.0
3. Holzbrücke (Wood Bridge)
cost: 1.5
4. Dom Brücke (Cathedral Bridge)
cost: 1.5
5. Grüne Brücke (Green Bridge)
cost: 1.0
6. Köttelbrücke (Dung Bridge)
cost: 1.0
7. Hohe Brücke (High Bridge)
cost:. 4.0
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A graph database is...
a database which organizes data using graph structures
Land:
A. Kneiphof (Island, Central)
area: 25
B. Altstadt (North)
area: 300
C. Lomse (Island, East)
area: 75
D. Löbenicht (South)
area: 350

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Why use a graph database?
good question

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Use a graph database...
to enjoy Graph benefits
• less friction between how you
think and how the data looks
• user-centric design
• better recommendations
• deeper insights
to solve Graph problems
• model connected data
• faster recursive queries
• path finding
• dependency analysis
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Follow the flow - buying sport shoes

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Panama papers:
simple model,
powerful outcome

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The Panama papers data
model...

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Roses are red,
facebook is blue,
No mutual friends,
So who are you?

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...or co-actors of co-actors
Friends of friends

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What are good graph
scenarios?

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Scenario 1: Does our problem involve understanding relationships between entities?
Identifying good graph scenarios
● Recommendations
● Fraud detection
● Finding duplicates
● Data lineage

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Scenario 2: Does the problem involve a lot of self-referencing to the same type of entity?
Identifying good graph scenarios
● Organisational
hierarchies
● Access management
● Social influencers
● Friends of friends

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Scenario 3: Does the problem explore relationships of varying or unknown depth?
Identifying good graph scenarios
● Supply chain
visibility
● Bill of Materials
● Network
management

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Scenario 4: Does our problem involve discovering lots of different routes or paths?
Identifying good graph scenarios
● Logistics and routing
● Infrastructure
management
● Dependency tracing

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How do you query a graph
database?
walk like a patternician

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Graph basics...
Node
● The main data element from which graphs are constructed
Jane car

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Graph basics...
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Node
● The main data element from which graphs are constructed
Relationship
● A link between two nodes. Has:
○ Direction
○ Type
● A node without relationships is permitted. A relationship without nodes is not
Jane car
OWNS

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Graph basics...
Node
Relationship
OWNS

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Graph basics...
Node
Relationship
:Person :Car
OWNS
Label
● Define node category (optional)

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Graph basics...
Node
Relationship
:Person :Car
OWNS
Label
● Define node category (optional)
● Can have more than one
:Asset

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Graph basics...
Node
Relationship
:Person :Car
OWNS
Label
● Define node category (optional)
● Can have more than one
Properties
● Enrich a node or relationship
● Simple key/value pairs!
name: Jane make: Volvo
model: V60
since: 2018
:Asset

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Cypher - the graph query language
A pattern-matching query language made for graphs

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Cypher - the graph query language
A pattern matching query language made for graphs
● Declarative
● Expressive
● Pattern-Matching

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Cypher - the graph query language
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A pattern matching query language made for graphs
● Declarative
● Expressive
● Pattern Matching
With ASCII ART ¯_(ツ)_/¯

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Use MATCH to retrieve nodes
//Match all nodes
MATCH (n)
RETURN n;
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Use MATCH to retrieve nodes
//Match all nodes
MATCH (n)
RETURN n;
//Match all nodes with a Person label
MATCH (n:Person)
RETURN n;
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Use MATCH to retrieve nodes
//Match all nodes
MATCH (n)
RETURN n;
//Match all nodes with a Person label
MATCH (n:Person)
RETURN n;
//Match all nodes with a Person label and property name is "Tom Hanks"
MATCH (n:Person {name: "Tom Hanks"})
RETURN n;
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//Return nodes with label Person and name property is "Tom Hanks" - Inline
MATCH (p:Person {name: "Tom Hanks"}) //Only works with exact matches
RETURN p;
Use MATCH and properties to retrieve nodes
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//Return nodes with label Person and name property is "Tom Hanks" - Inline
MATCH (p:Person {name: "Tom Hanks"}) //Only works with exact matches
RETURN p;
//Return nodes with label Person and name property equals "Tom Hanks"
MATCH (p:Person)
WHERE p.name = "Tom Hanks"
RETURN p;
Use MATCH and properties to retrieve nodes
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36. © Neo4j, Inc. 2021 CC By-SA 4.0
//Return nodes with label Person and name property is "Tom Hanks" - Inline
MATCH (p:Person {name: "Tom Hanks"}) //Only works with exact matches
RETURN p;
//Return nodes with label Person and name property equals "Tom Hanks"
MATCH (p:Person)
WHERE p.name = "Tom Hanks"
RETURN p;
//Return nodes with label Movie, released property is between 1991 and 1999
MATCH (m:Movie)
WHERE m.released > 1990 AND m.released < 2000
RETURN m;
Use MATCH and properties to retrieve nodes
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Extending the MATCH
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//Find all the movies Tom Hanks acted in
MATCH (:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m:Movie)
RETURN m.title;
Extending the MATCH
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//Find all the movies Tom Hanks acted in
MATCH (:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m:Movie)
RETURN m.title;
//Find all of the co-actors Tom Hanks have worked with
MATCH (:Person {name:"Tom Hanks"})-->(:Movie)<-[:ACTED_IN]-(coActor:Person)
RETURN coActor.name;
Extending the MATCH
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How is this transformative?
graph thinking

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Bridges:
1. Krämerbrücke (Trader's Bridge)
cost: 1.0
2. Schmiedebrücke (Forged Bridge)
cost: 1.0
3. Holzbrücke (Wood Bridge)
cost: 1.5
4. Dom Brücke (Cathedral Bridge)
cost: 1.5
5. Grüne Brücke (Green Bridge)
cost: 1.0
6. Köttelbrücke (Dung Bridge)
cost: 1.0
7. Hohe Brücke (High Bridge)
cost:. 4.0
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A graph database is...
a database which organizes data using graph structures
Districts:
A. Kneiphof (Island, West)
area: 25
B. Altstadt (North)
area: 300
C. Lomse (Island, East)
area: 75
D. Löbenicht (South)
area: 350

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A graph database is optimized for working with the
connections in data.
All data is connected.

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All data is connected?
• if you have people, you have relationships
• if you have places, you have roads
• if you have messages, you have replies
• if you have transactions, you have… transactions
• if you have things, you have connections
within those things and to other things
• if you have data, you can always discover or add connections

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If you have connections,
you have new ways
to understand,
to discover,
to enrich
the data you already have.

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Thank you!
Want more! Take this course: Neo4j Overview
https://neo4j.com/graphacademy/training-overview-40/
Next session: Intro to Cypher
Please steal these slides! But give us credit. :)