This document discusses graph data science and Neo4j's capabilities. It describes how Neo4j can help simplify graph data science through its native graph database, graph data science library, and data visualization tool. Example use cases are also provided that demonstrate how Neo4j has helped companies with fraud detection, customer journey analysis, supply chain management, and patient outcomes.

1. Neo4j
Graph Data Science
('Riccardo Ciarlo', 'riccardo.ciarlo@neo4j.com') -[:IS_MEMBER_OF]-> ('Country Manager', 'Neo4j', 'Italy')
('Marco Bessi', 'marco.bessi@neo4j.com') -[:IS_MEMBER_OF]-> ('Field Engineer PreSale', 'Neo4j', 'Italy')

2. 2
We help the world make sense of data
The leader in
Graph Databases
Creator of the 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 & Malmo
7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Industry Leaders use Neo4j

3. 3
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
360 view of customer
Compliance
Optimize Operations
Data Science
AI & ML
Fraud Prediction
Patient Journey
Customer Disambiguation
Transforming Industries

4. Networks of People Transaction Networks
Bought
B
ou
gh
t
V
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e
w
e
d
R
e
t
u
r
n
e
d
Bought
Knowledge Networks
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
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o
f
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
K
n
o
w
s
Knows
Knows
K
n
o
w
s
Connections in data are as valuable as
the data itself

5. Predictive
Maintenance
Churn
Prediction
Fraud
Detection
Life
Sciences
Personalized
Recommendations
Cybersecurity
Disambiguation &
Segmentation
Search &
Master Data Mgmt.
Graph Data Science applications
Just a few examples…

6. What is:
Data science Graph data science
Data science is an
interdisciplinary field
that uses scientific
methods, processes,
algorithms and
systems to extract
knowledge and insights
from structured and
unstructured data.
Graph Data Science is a
science-driven
approach to gain
knowledge from the
relationships and
structures in data,
typically to power
predictions.
Data scientists use data to answer
questions.
Data scientists use relationships to
answer questions.

7. 7
Data science: it’s complicated
Dozens of
libraries,
hundreds of algos
& no docs!
How do we
shape data into a
graph in the first
place?
We’ve picked a
library...good
luck learning the
syntax
What? We have
to build the
entire ETL pipeline
for this?
Are the results
right? How do
we get into
production?
Data
Modeling
Which
Algorithms?
Learn
Syntax
Reshape
Data
What
Now?

8. 8
Simplify your experience!
Dozens of
libraries,
hundreds of algos
& no docs!
We’ve picked a
library...good
luck learning the
syntax
What? We have
to build the
entire ETL pipeline
for this?
Are the results
right? How do
we get into
production?
Data
Modeling
Which
Algorithms?
Learn
Syntax
Reshape
Data
What
Now?
We have validated
algos, clear docs,
& tutorials
Neo4j syntax is
standardized
and simplified
Seamlessly
reshape data with
1 command
Simply write results
to Neo4j & move to
production
With Neo4j
it’s already a
graph

9. Evolution of
Graph Data Science

10. Evolution of Graph Data Science
Decision
Support
Graph Based
Predictions
Graph Native
Learning
10
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
Knowledge
Graphs
Graph
Analytics

11. Evolution of Graph Data Science
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
11
Graph
Analytics
Knowledge
Graphs
Graph search
and queries
Support domain
experts
Fast, local decisioning and pattern matching
You know what you
are looking for and
making a decision

12. Evolution of Graph Data Science
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
12
Knowledge
Graphs
Graph
Analytics
Graph queries &
algorithms for
offline analysis
Understanding
Structures
Global analysis and iterations
You are learning the
overall structure of a
network, updating
data and predicting

13. Evolution of Graph Data Science
Graph
Embeddings
Graph
Networks
13
Knowledge
Graphs
Graph
Analytics
Graph Feature
Engineering
Graph algorithms
& queries for
machine learning
Improve Prediction
Accuracy
Take advantage of
hardened, validated graph
algorithms that enable
reasoning about network
structure.

14. Evolution of Graph Data Science
14
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
Knowledge
Graphs
Graph
Analytics
Graph embeddings
for dimensionality
reduction
Predictions on
complex structures
Embedding transforms graphs into a
feature vector, or set of vectors, describing
topology, connectivity, or attributes of nodes
& relationships in the graph

15. Neoj4 for
Graph Data Science

16. 16
for Graph Data Science (GDS)
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

17. 17
Neo4j Database: native graph technology
Enterprise-grade native graph database and tooling:
▪ Store, reveal and query data relationships
▪ Traverse and analyze any levels of depth in real-time
▪ Add context to AI systems and network structures to data science
• 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
• Index-free adjacency
Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics

18. 18
Neo4j GDS Library
Robust Graph Algorithms
▪ Compute connectivity metrics and learn the topology of
your graph
▪ Highly parallelized and scale to 10’s of billions of nodes
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
Mutable In-Memory
Workspace
Computational Graph
Native Graph Store

19. Neo4j GDS Library: Graph algorithms categories
19
Pathfinding
and Search
Centrality Community
Detection
Heuristic
Link Prediction
Similarity
Determines the
importance of
distinct nodes in
the network.
Detects group
clustering or
partition.
Evaluates how
alike nodes are by
neighbors and
relationships.
Finds optimal
paths or
evaluates route
availability and
quality.
Estimates the
likelihood of
nodes forming a
future
relationship.
50+ graph algorithms in Neo4j
Embeddings
Learns graph
topology to
reduce
dimensionality for
ML

20. Neo4j Bloom: Built-in data visualization
▪ Explore graphs visually
▪ Prototype faster
▪ Visualize and discover
▪ Easy for non-technical
users

21. 21
Neo4j Graph Data Science
From Analytics to Graph-Native Machine Learning
Graph algorithms to uncover trends
and patterns
Patterns
Pointers Queries to answer questions with connected data
Predictions
Graph-native ML to use the topology
of your graph to uncover new facts

22. GDS 1.5
What’s new

23. Memory
Compression
75% smaller
memory footprint
using an
enterprise graph
projection
Footprint
Production
Algorithms
Performance
improvements &
new standard API
for pathfinding
Pathfinding
Machine
Learning
Persistent &
publishable
models,
node classification
& link prediction
Workflows
Customizable
Algorithms
Pregel
enhancements &
algorithms
(e.g. HITS & SLLPA)
Pregel API
Enhancements in GDS v1.5

24. Graph-Native
Feature
Engineering
Train
Predictive
Model
Queries
Algorithms
Embeddings
1. Model Type
2. Property
Selection
3. Train & Test
4. Model
Selection
Supervised ML workflow in Neo4j
Apply Model to
Existing / New
Data
Use Predictions
for Decisions
Use Predictions
to Enhance
the Graph
Publish & Share
Store Model in
Database

25. Use cases
25

26. 26
Top Graph Data Science Applications
Fraud
Marketing
Customer
Journey
in Financial Services and Banking
• First party & synthetic
identity fraud
• Fraud rings
• Money laundering
• Disambiguation
• Recommendations
• Customer segmentation
• Churn prediction

27. 27
Top Graph Data Science Applications
Market-To
Supply Chain
Logistics
in Marketing and Supply Chain
• Disambiguation
• Recommendation
• Customer segmentation
• Logistics and routing
• Predictive fulfillment
• Risk identification
• Supply chain driven
product design

28. Media conglomerate with $3.2
Billion revenue
Parent of: People, Travel+Leisure,
Better Homes & Gardens...
28
Illuminating the Anonymous
Neo4j GDS for Identify Disambiguation
• Connect various data streams with 4.4 TB of data (14Bn nodes)
• Graph algorithms to find unique users by behavior
• 163Mn unique profile with richer & longer lived data
• 612% Increase in visits per profile
Challenge: Marketing in the Dark
• Anonymous across sites & devices with aging cookies
• External data is expensive and difficult to validate

29. 29
Top Graph Data Science Applications
Discovery
Patient Care
Regulatory
Compliance
in Healthcare and Life Sciences
• Drug repurposing
• Knowledge graph
completion
• Risk identification &
spread
• Patient journey
• Personalized care
• Contact tracing

30. Medical device manufacturer with
10.74B annual revenue
Manufacture products like
pacemakers, stents and heart
valves, all the way through
diagnostic tests. Integrated
development, design,
manufacture, and sales.
30
Improving Reliability
Neo4j GDS for supply chain & issues prediction
Simple data model: parts, finished product, and failures
• Knowledge Graph to support robust queries
• Centrality algorithms to rank nodes based on their proximity to failures,
similarity to find vulnerable components
• Creating new data from connections in Neo4j
Challenge: Predicting and preventing failures
• Integrated supply chain: from raw materials to complex devices
• Inconsistent analysis, unable to pinpoint cause of failures

31. Global pharmaceutical with
$22.1Billion revenue
Focus on oncology, cardiovascular,
renal, metabolism, & respiratory
31
Improving Patient Outcomes
Neo4j GDS to Map & Predict Patient Journeys
• 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 earlier influence points to guide and assist
Challenge: Better intervention for complex diseases
• Complex diseases develop over years with many touch points
• How can we intervene faster & improve outcomes?

32. Q&A
('Riccardo Ciarlo', 'riccardo.ciarlo@neo4j.com') -[:IS_MEMBER_OF]-> ('Country Manager', 'Neo4j', 'Italy')
('Marco Bessi', 'marco.bessi@neo4j.com') -[:IS_MEMBER_OF]-> ('Field Engineer PreSale', 'Neo4j', 'Italy')