Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work. Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records. We'll discuss the primary use cases for graph databases Explore the properties of Neo4j that make those use cases possible Look into the visualisation of graphs Introduce how to write queries. Webinar, 23 July 2020

1. An Introduction to
Neo4j
Anthony Flynn, Sales Director UK & Ireland

2. 7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
Neo4j - The Graph Company
The Industry’s Largest Dedicated Investment in Graphs
2
Creator of the Label
Property Graph and
Cypher language at the
core of the GQL ISO project
Thousands of Customers
World-Wide
HQ in Silicon Valley, offices
include London, Munich,
Paris & Malmö
Industry Leaders use Neo4j
On-Prem
DB-as-a-Service
In the Cloud

3. Connections in Data are as
valuable as the Data itself
Networks of People Transaction Networks
Bought
Bought
Viewed
Returned
Bought
Knowledge Networks
Plays
Lives_in
In_sport
Likes
Fan_of
Plays_for
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Knows
Knows
Knows
Knows

4. 4
Harnessing Connections Drives Business Value
Enhanced Decision
Making
Hyper
Personalization
Massive Data
Integration
Data Driven
Discovery & Innovation
Product Recommendations
Personalized Health Care
Media and Advertising
Fraud Prevention
Network Analysis
Law Enforcement
Drug Discovery
Intelligence and Crime
Detection Product
& Process Innovation
360º view of customer
Compliance
Optimize Operations
Data Science
AI & ML
Fraud Prediction
Patient Journey
Customer Disambiguation
Transforming Industries

5. Neo4j is an enterprise-grade native graph database and associated tools:
• Store, reveal and query data and data relationships
• Traverse and analyze data to many levels of depth in real-time
• Add context to AI systems and network structures to data science
5
Native Graph Technology
• Performance
• ACID Transactions
• Schema-free Agility
• Graph Algorithms
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Enterprise Scale
• Graph Adoption
Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics

6. 6
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half
the hardware compared to Oracle, which
Neo4j replaced
• Large postal service with over 500k
employees
• Neo4j routes 7M+ packages daily at peak,
with peaks of 5,000+ routing operations
per second.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time

7. 7
• The media conglomerate Meredith uses
Neo4j to turn data about its largely
anonymous website visitors into customer
profiles by graphing the data into billions of
nodes and then applying machine learning to
it.
• Almost 70% of Credit Card fraud was missed
• +1B Nodes and +1B Relationships to analyse
• Graph analytics with queries & algorithms
help find $10’s of millions of fraud in 1st year
Improving Analytics, ML & AI Across Industries
Meredith Marketing
to the Anonymous
Financial Fraud
Detection & Recovery Top 10 Bank
• Early intervention project with 3 years of
visits, tests & diagnosis with 10’s of Billions
of records
• Finding similarities in patient journeys
• Graph algorithms for identifying
communities & best intervention points
AstraZeneca
Patient Journeys

8. Graph Technology
8

9. The Whiteboard Model Is the Physical Model
9

10. CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Latitude: 37.5629900°
Longitude: -122.3255300°
Nodes
• Can have Labels to classify nodes
• Labels have native indexes
Relationships
• Relate nodes by type and direction
Properties
• Attributes of Nodes & Relationships
• Stored as Name/Value pairs
• Can have indexes and composite indexes
• Visibility security by user/role
Neo4j Invented the Labeled Property Graph Model
MARRIED TO
LIVES WITH
OW
NS
PERSON PERSON
10

11. Cypher: Powerful & Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE

12. MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “Jane Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Express Complex Queries Easily with Cypher
Find all direct reports and how
many people they manage,
up to 3 levels down
Cypher Query
SQL Query

13. Relational Versus Graph Models
Relational Model Graph Model
KNOWS
KNOWS
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person FriendPerson-Friend
ANDREAS
DELIA
TOBIAS
MICA

14. Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
Native Graph Technology for Applications & Analytics

15. APPLICATION
SERVERS
15
Neo4j Clustering - Causal Cluster
Replica Servers
Query, View
Core Servers
Synced Cluster
CORE
SERVERS READ
REPLICAS
Async
Replication
Writes
(transactions)
Reads
(Graph
Queries)

16. Robust Graph Algorithms
• Run on the loaded graph to compute metrics about the topology
and connectivity
• Highly parallelized and scale to 10’s of billions of nodes
16
The Neo4j GDS Library
Mutable In-Memory
Workspace
Computational Graph
Native Graph Store
Efficient & Flexible Analytics
Workspace
• Automatically reshapes transactional graphs
into an in-memory analytics graph
• Optimized for analytics with global traversals
and aggregation
• Create workflows and layer algorithms

17. +50 Algorithms in the Neo4j GDS Library
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• A* Shortest Path
• Yen’s K Shortest Path
• Minimum Weight Spanning Tree
• K-Spanning Tree (MST)
• Random Walk
• Degree Centrality
• Closeness Centrality
• CC Variations: Harmonic, Dangalchev,
Wasserman & Faust
• Betweenness Centrality & Approximate
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Euclidean Distance
• Cosine Similarity
• Node Similarity (Jaccard)
• Overlap Similarity
• Pearson Similarity
• Approximate KNN
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
...and also Auxiliary Functions:
• Random graph generation
• Encoding
• Distributions & metrics
17

18. for Graph Data Science™
Neo4j Graph Data
Science Library
Scalable Graph Algorithms
& Analytics Workspace
Native Graph
Creation & Persistence
Neo4j
Database
Visual Graph Exploration
& Prototyping
Neo4j
Bloom
Practical Integrated Intuitive

19. 19
Perspective
Search
Visualize
Explore
Inspect
Edit
Explore & Collaborate
with Neo4j Bloom
Explore Graphs Visually
Prototype Concepts Faster
Collaborate Across Teams

20. Neo4j Bloom’s
Intuitive User Interface
20
Search with type-ahead
suggestions
Flexible Color, Size and Icon
schemes
Visualize, Explore and Discover
Pan, Zoom and Select
Property Browser and editor

21. Neo4j in the Cloud
21

22. Neo4j Cloud offerings to suit every need
22
Database-as-a-service Self-hosted Cloud Managed Services (CMS)
Cloud-native service
Zero administration Pay-as-
you-go
Self-service deployment
Cloud-native stack
No access to underlying infra
and systems.
Self hosted and managed
Any cloud (AWS, GCP, Azure)
Bring-your-own-license
Self-manage software, infra
in own private cloud
Own data, tenant, security
>50% deploy this way
White-glove fully managed
service by Neo4j experts
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud

23. Neo4j Aura: Built for the best developer experience
Neo4j’s open source roots backed by the strongest graph community helps deliver the best developer experience to rapidly build
rich graph-powered applications
23
Easy
Start in minutes
Automatic upgrades, patches
Scale on-demand instantly
Zero downtime
Powerful
Lightning-fast queries with
Native graph engine
Flexible “whiteboard”
data model
Cypher - expressive, efficient
and easy!
Broad language driver support
Reliable
End-to-end encrypted
Always ON
Globally available on world-class
infrastructure
Self-healing, durable
ACID compliant
Affordable
Pay-as-you-go
Capacity based pricing
Billing by the hour, starting
as low as 9¢/hr
Simple and predictable bills

24. Neo4j Cloud Managed Services (CMS)
Enterprise-class, white-glove managed services for day-to-day operations,
service and support of your Neo4j environment
Dedicated team,
always on-call
Advanced monitoring and
preventative maintenance
Enterprise-grade security
and compliance
24x7x365 remote services
and support
Big Three clouds, private
cloud, or on-premises
Your data in your
infrastructure, fully
controlled versioning

25. The CMS Advantage
Focus on
Innovation
… while we manage
your day-to-day
infrastructure
operations
Achieve Faster
Time-to-value
… with experts
to manage your
environment from day
one. Minimize hiring, in-
house training, and ramp-
up.
Reduce your
Risk
… and meet your
security, compliance
and business continuity
needs with proven best
practices.
Accelerate your
Cloud Journey
… by enabling a fully
managed enterprise
cloud environment and
moving your production
Neo4j environment
within days.

26. Real-World
Success Stories
26

27. Recommendations Dynamic Pricing IoT-applicationsFraud Detection
Real-Time Transaction Applications
Generate and
Protect Revenue
Customer
Engagement
Metadata and Advanced Analytics
Data Lake
Integration
Knowledge
Graphs for AI
Risk
Mitigation
Generate
Actionable Insights
Network
Management
Supply Chain
Efficiency
Identity and Access
Management
Internal Business Processes
Improve Efficiency
and Cut Costs
27
Graph Use Cases by Value Proposition

28. Dun & Bradstreet
Neo4j for Tracking Beneficial
Ownership
Background
● Regulations and requirements around beneficial
ownership
● Needed to let B2B clients book new business promptly
via accelerated due diligence investigations
Business Problem
● Investigations call for highly trained staff, and this activity is
hard to scale. A single query might tie up key people for 10-15
days, resulting in lost revenue
Solution and Benefits
● Use Neo4j to quickly query historic relationships between
business owners and companies
● Query responses take milliseconds versus days of skilled
manual research

29. Adobe Behance
Social Network of 10M
Graphic Artists
Background
● Social network of 10M graphic artists
● Peer-to-peer evaluation of art and works-in-progress
● Job sourcing site for creatives
● Massive, millions of updates (reads & writes) to Activity Feed
● 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
● Artists subscribe, appreciate and curate “galleries” of works of their own
and from other artists
● Activities Feed is how everyone receives updates
● 1st implementation was 150 MongoDB instances
● 2nd implementation shrunk to 48 Cassandras, but it was still too slow and
required heavy IT overhead
Solution and Benefits
● 3rd implementation shrunk to 3 Neo4j instances
● Saved over $500k in annual AWS fees
● Reduced data footprint from 50TB to 40GB
● Significantly easier to introduce new features like, “New projects in your
Network”

30. US Army / Calibre
Systems
Equipment Logistics
Background
● US IT consulting firm helped US Army streamline equipment
deployments and maintenance spending
● Saving lives by improving the operational readiness of Army
equipment like tanks, radios, transports, aircraft, weaponry, etc.
Business Problem
● Needed to modernize procurement, budget and logistics processes for
equipment & spare parts
● Millions of connections among a tank’s bill-of-materials, for example
● Improve “what if” cost calculations when planning missions and troop
deployments
● Mainframe systems required over 60 man-hrs to calculate changes…
planning took too long.
Solution and Benefits
● 118M nodes & 185M relationships
● Shed cost estimation times by 88%
● Improved parts delivery timing and accuracy
● DBA labor required dropped by 77%
● Equipment TCO more predictable
● Safer soldiers

31. Caterpillar
Heavy Equipment
Manufacturing
Background
● Fortune 100 heavy equipment manufacturer
● 27 Million warranty & service documents parsed
● Foundation for AI-based supply chain management
Business Problem
● Improve maintenance predictability
● Need a knowledge base for 27 million warranty documents and
maintenance orders
● Graphs gather context for AI to identify ‘prime examples’ of connections
among parts, suppliers, customers and their mechanics anticipate when
equipment will need servicing and by whom.
Solution and Benefits
● Text to knowledge graph
● Common ontology for complaints, symptoms & parts
● Anticipates when equipment will need servicing
● Improves customer and brand satisfaction
● Maximizes lifespan and value of equipment

32. Improving Patient Outcomes
Global pharmaceutical with
$22.1Billion revenue
Focus on oncology,
cardiovascular, renal,
metabolism, & respiratory
32
Neo4j GDS to Map & Predict Patient Journeys
• Kidney disease intervention project
• 3 yrs of visits, tests & diagnosis with 10’s of Bn of records
• Knowledge Graph, graph queries & algorithms
• Community detection to help find similarities over time
• Finding influence points where experienced physicians may be
able to guide and assist
• Looking forward to path based embeddings
Challenge: Better intervention for complex diseases
• Complex diseases develop over years with many, many doctor
visits, tests and evolving diagnosis
• How to identify early warnings, intervene faster & improve
outcomes?
• No two patients are the same, so how are similarities found?

33. Let’s Do Something Amazing
Together…
Try Neo4j today: https://neo4j.com/sandbox/
Free training and education: https://neo4j.com/graphacademy/
Contact us: https://neo4j.com/contact-us/