The document is an introduction to Neo4j presentation that covers what a graph database is, why graphs are useful, spotting good use cases for graph databases, the components of a property graph database, querying graphs using Cypher, and ways to continue learning about Neo4j through online resources and training. It includes examples of querying a movie graph database and previewing StackOverflow data modeled as a graph.
Graph Machine Learning in Production with Neo4jNeo4j
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This comes down to three things: 1. Getting your data into a graph
2. Use graph algorithms to find what’s important
3. Use machine learning to make predictions on your graph
We covered these three key steps with code examples and discussed some key considerations when moving your ML workloads to production.
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What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
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In our presentation at Data Innovation Summit 2023, we explained how you could accelerate AI and machine learning innovation by using graph data science.
This comes down to three things: 1. Getting your data into a graph
2. Use graph algorithms to find what’s important
3. Use machine learning to make predictions on your graph
We covered these three key steps with code examples and discussed some key considerations when moving your ML workloads to production.
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
The path to success with Graph Database and Graph Data ScienceNeo4j
What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
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These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Natural Language Processing with Graph Databases and Neo4jWilliam Lyon
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...Neo4j
Large Language models are amazing but are also black-box models that often fail to capture and accurately represent factual knowledge. Knowledge graphs, by contrast, are structural knowledge models that explicitly represent knowledge and, indeed, allow us to detect implicit relationships. In this talk we will demonstrate how LLMs can be improved by Knowledge Graphs, and how LLM’s can augment Knowledge Graphs. A perfect couple!
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Natural Language Processing with Graph Databases and Neo4jWilliam Lyon
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...Neo4j
Large Language models are amazing but are also black-box models that often fail to capture and accurately represent factual knowledge. Knowledge graphs, by contrast, are structural knowledge models that explicitly represent knowledge and, indeed, allow us to detect implicit relationships. In this talk we will demonstrate how LLMs can be improved by Knowledge Graphs, and how LLM’s can augment Knowledge Graphs. A perfect couple!
Heard about graph databases? Curious about what they are and how they work? Want to know where they're best used? Then this is the session for you!
In this workshop we will:
- Introduce you to graph databases
- Cover approaches for identifying graph-shaped problems
- Get our hands on our very first graph database experience where we will load and query data, using Neo4j Aura Free
We'll also cover what resources are available, and how to continue your graph journey.
Enabling GenAI Breakthroughs with Knowledge GraphsNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Neo4j GraphSummit Copenhagen - The path to success with Graph Database and Gr...Neo4j
What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
MongoDB and DigitalOcean Automation with Cloud ManagerJay Gordon
Cloud Manager automation is an easy way to create new MongoDB deployments without having to do much of the underlying configuration work. This talk is how you can get started using automation on your droplets.
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Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.
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Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
1. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
Introduction to Neo4j -
a hands-on crash course
Michael Hunger
Developer Relations
@mesirii
dev.neo4j.com/forum
dev.neo4j.com/chat
2. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
This is an interactive session!
Tell us where you're from!
And please ask Questions
as we go along!
In any of the chats
3. Neo4j, Inc. All rights reserved 2022
In this session
We will cover:
• What is a graph and why they are amazing
• Spotting good graph scenarios
• Property graph database anatomy and introduction to Cypher
• Hands-on: the movie graph on Neo4j AuraDB Free
◦ dev.neo4j.com/aura-login
• Sneak peek: stackoverflow data
• Continuing your graph journey
Useful reference: https://dev.neo4j.com/rdbms-gdb
4. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
Because it takes a few minutes to launch
Let's get started on
AuraDB Free
dev.neo4j.com/aura-login
Ask in the chat if something doesn't work!
5. Neo4j, Inc. All rights reserved 2022
Time to have a go! With AuraDB Free
We are going to:
1. Go to dev.neo4j.com/aura-login
2. Sign in & click “Create a database”
3. Selected “AuraDB Free” database size
4. Give your database a name
5. Select a Region
6. Select Movies Database
7. Click “Create Database”
8. Make a copy of the generated password - keep it safe!
9. Wait for 3-5 minutes
Can’t access Aura Free? No problem! Use Neo4j Sandbox:
• Go to dev.neo4j.com/try
• Sign in & click “Blank sandbox”
11. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
What is a graph?
versus
12. Neo4j, Inc. All rights reserved 2022
A graph is...
...a set of discrete entities, each of which has some set of relationships with the
other entities
Seven Bridges of Konigsberg problem. Leonhard Euler, 1735
13. Neo4j, Inc. All rights reserved 2022
Anything can be a graph - do you have examples too?
the Internet a water molecule
H
O
H
14. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
Why are graphs amazing?
16. Neo4j, Inc. All rights reserved 2022
Follow the flow - buying trainers
17. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
Panama, paradise, pandora papers:
simple model, powerful outcome
18. Neo4j, Inc. All rights reserved 2022
18
The ICIJ Investigations
data model...
19. Neo4j, Inc. All rights reserved 2022
Roses are red,
facebook is blue,
No mutual friends,
So who are you?
20. Neo4j, Inc. All rights reserved 2022
Friends of friends
...or co-actors of co-actors
21. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
What are good graph scenarios?
22. Neo4j, Inc. All rights reserved 2022
Scenario 1: Does our problem involve understanding relationships between
entities?
Identifying good graph scenarios
● Recommendations
● Fraud detection
● Finding duplicates
● Data lineage
● Social Networks
23. Neo4j, Inc. All rights reserved 2022
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
24. Neo4j, Inc. All rights reserved 2022
Scenario 3: Does the problem explore relationships of varying or unknown
depth?
Identifying good graph scenarios
● Supply chain
visibility
● Bill of Materials
● Network
management
● Routing
25. Neo4j, Inc. All rights reserved 2022
Scenario 4: Does our problem involve discovering lots of different routes or
paths?
Identifying good graph scenarios
● Logistics and routing
● Infrastructure
management
● Dependency tracing
26. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
So what does a (property) graph
look like?
27. Neo4j, Inc. All rights reserved 2022
Node (Vertex)
● The main data element from which graphs are constructed
27
Graph components
Jane bike
28. Neo4j, Inc. All rights reserved 2022
28
Graph components
Node (Vertex)
● The main data element from which graphs are constructed
Relationship (Edge)
● A link between two nodes. Has:
○ Direction
○ Type
● A node without relationships is permitted. A relationship without nodes is not
Jane OWNS bike
29. Neo4j, Inc. All rights reserved 2022
29
Property graph database
Node (Vertex)
Relationship (Edge)
OWNS
30. Neo4j, Inc. All rights reserved 2022
30
Property graph database
Node (Vertex)
Relationship (Edge)
:Person :Bike
OWNS
Label
● Define node role (optional)
31. Neo4j, Inc. All rights reserved 2022
31
Property graph database
Node (Vertex)
Relationship (Edge)
:Person
:Vehicle
:Bike
OWNS
Label
● Define node role (optional)
● Can have more than one
32. Neo4j, Inc. All rights reserved 2022
32
Node (Vertex)
Relationship (Edge)
:Person OWNS
Label
● Define node role (optional)
● Can have more than one
Properties
● Enrich a node or relationship
● No need for nulls!
name: Jane make: Specialized
model: Crux Pro
since: 2018
Property graph database
:Vehicle
:Bike
33. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
And now query the graph
together!
34. Neo4j, Inc. All rights reserved 2022 34

Cypher
A pattern-matching query language made for graphs
35. Neo4j, Inc. All rights reserved 2022 35

Cypher
A pattern matching query language made for graphs
• Declarative
• Expressive
• Pattern-Matching
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Cypher
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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//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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//Find all the movies Tom Hanks is connected to
MATCH (:Person {name:"Tom Hanks"})--(m:Movie)
RETURN m.title;
Extending the MATCH
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//Find all the movies Tom Hanks is connected to
MATCH (:Person {name:"Tom Hanks"})--(m:Movie)
RETURN m.title;
//Find all the movies Tom Hanks directed and order by latest movie
MATCH (:Person {name:"Tom Hanks"})-[:DIRECTED]->(m:Movie)
RETURN m.title, m.released ORDER BY m.released DESC;
Extending the MATCH
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//Find all the movies Tom Hanks is connected to
MATCH (:Person {name:"Tom Hanks"})--(m:Movie)
RETURN m.title;
//Find all the movies Tom Hanks directed and order by latest movie
MATCH (:Person {name:"Tom Hanks"})-[:DIRECTED]->(m:Movie)
RETURN m.title, m.released ORDER BY m.released DESC;
//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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//Uniquely create a person node called "Tom Hanks"
MERGE (p:Person {name:"Tom Hanks"});
MERGE
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//Create a person node called "Tom Hanks"
MERGE (p:Person {name:"Tom Hanks"});
//Create an ACTED_IN relationship between "Tom Hanks" and "Apollo 13"
MATCH (p:Person {name:"Tom Hanks"}), (m:Movie {title:"Apollo 13"})
MERGE (p)-[:ACTED_IN]->(m);
MERGE
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Nodes and relationships at a glance
Description Node Relationship
Generic () -- --> -[]-
With a reference (n) -[r]-
With a node label or rel
type
(:Person) -[:ACTED_IN]-
With a label/type and an
inline property
(:Person {name: 'Bob'}) -[:ACTED_IN {role: 'Dave'}]-
With a variable,
label/type and an inline
property
(p:Person {name: 'Bob'})
-[r:ACTED_IN {role:
'Rob'}]-
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Neo4j, Inc. All rights reserved 2022
Stackoverflow Demo
:play sandbox/stackoverflow
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Neo4j, Inc. All rights reserved 2022
What Else is There?
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Connectors
• Spark
• Kafka
• JDBC
What else is there?
Drivers
• Python
• JavaScript
• Java
• .Net
• Go
Data Science
• Graph Data Science Library
• Bloom Visualization
Libraries / Integrations
• neo4j/graphql
• Spring Data Neo4j
• neosemantics (RDF)
• APOC (utility)
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Neo4j, Inc. All rights reserved 2022
So how do I continue my graph
journey?
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A training class each week - Tuesdays, 3pm UTC
09 Mar: Getting Started with Neo4j
Bloom
16 Mar: Build APIs with Neo4j
GraphQL Library
23 Mar: Create a Knowledge
Graph: A Simple ML Approach
Read all about it!
dev.neo4j.com/training
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Neo4j, Inc. All rights reserved 2022
Free online training and
certification:
• dev.neo4j.com/learn
• dev.neo4j.com/datasets
How to, best practices, hands on
and community stories:
• dev.neo4j.com/videos
Come say hello :)
• dev.neo4j.com/chat
• dev.neo4j.com/forum
Continue your journey
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Neo4j, Inc. All rights reserved 2022
Michael Hunger
Developer Relations
@mesirii
michael@neo4j.com
Join the conversation at dev.neo4j.com/forum