GraphSummit Toronto: Keynote - Innovating with Graphs

© 2022 Neo4j, Inc. All rights reserved.
1
Dr. Jim Webber
Chief Scientist, Neo4j
Graph to the
Future!

2

Overview
• About Neo4j
• Intro to Graphs
• Neo4j Graph Platform
• RDBMS/NoSQL
• Graph Data Science
• Looking to the Future

Neo4j: A Rich History of Graph Innovation
4
2020 - 2022
● Graph-RBAC Security, the First and
Most Advanced of Its Kind
● First Native & Fully Integrated Graph
Data Science Offering
● First In-Graph Machine Learning
Technology
● Neo4j Fabric, the First Enterprise
Graph Scaling Architecture for
Sharding & Federation
2015 - 2019
● openCypher: the De Facto Open
Source Graph Standard
● Graph Algorithms for Data Science
● Bloom for Rich Data Visualization and
Exploration
● Active Participant in ISO GQL
Standard, initiated by Neo4j
● AuraDB: First Native Graph DB as-a-
Service
2010 - 2014
● Pioneered the Graph Database Category
● First Native Graph Database:
Open Source, Built for Property Graphs
● Introduced Cypher Graph Query
Language
● Evolved Property Graph Model with
Labels, Geospatial & More
4

Graph technology is fueling discovery and
transformation in every field
Decision
Analysis
Customer
Experience
Data
Unification
Personalization Discovery
Fraud Prevention
Network Analysis
Forecasting
Patient/Customer Journey
Behavior Prediction
Data Disambiguation
Operations Optimization
Customers/Data 360
Compliance
Product Recommendations
Media and Advertising
Personalized Health
Drug Discovery
Product/Process Innovation
Intelligence & Security
5

6
Since 2013
100m+ Neo4j Downloads
250k+ Community Members
Graph DB
Source: DB Engines
The Fastest Growing Database Market for a Decade

The Graph Data Platform
Market Leader
1 Enterprises with >$1B annual revenue
2
Source: DBEngines
● HQ in Silicon Valley, with global footprint
● Over 300 global enterprise1
customers
800+ total customers
● Category creator and leader in Graph
Databases, the fastest growing category2 in
all of data management
Funding
$400M+
in 2021
~1000
Employees EOY
Growing at
50%+
YoY
2.5M+
Downloads
80%+
Active Developers YoY Growth
7
#1 Most Popular
Graph Database with Developers
200k+
Developers

for Graph Data Platforms, Q4 2020
8
The Forrester Wave™
Neo4j: The Leader in a
Vibrant, Growing Market
8

75+
Insurance
of the Top 10
8
Banks
of the Top 20
North American
20
Automakers
of the Top 10
8
Retailers
of the Top 10
7 Telcos
of the Top 10
7
Hotels
of the Top 5
3
Aircraft
Manufacturers
of the Top 5
3
Pharmaceuticals
of the Top 5
5
9

10
Why Customers Choose Neo4j
Development & Data Science Agility
Developer Tooling
No-code, Built in query browser, data
visualizer
Data Science
Most graph algorithms,
Supervised ML
Standards & Open Source
GQL, OpenCypher
Cypher, GraphQL
Powerful, Intuitive, Flexible options
Battle Tested Foundation
Native Graph Architecture
Uncompromised Scale & Performance
Deep Domain Expertise
Category creator, Largest graph dev
investment, Focus
Largest Graph Community
250k+ developers and data scientists,
partners with leading CSPs and integrators.
Enterprise Proven, Trusted
1000s of deployments, powering global
brands
Proven Enterprise Performance, Scale, Security and Reliability

Hold on. What’s a
graph, Jim?

12
But first…ground rules!
This
is
a
graph
This
is
a
chart

13
You land here, at LHR
Neo4j London

14
Neo4j London

15
Neo4j London

The Labeled Property Graph Model
Nodes
• Can have Labels to classify nodes
Relationships
• Relate nodes by type and direction
• Jim likes soccer, soccer does not like Jim
Properties
• Stored as name/value pairs
Performance
• Traversals are always O(1)
• Query latency depends on how much of the
graph you want to explore
• It does not depend on data set size
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°
SISTER
BROTHER
O
W
N
S
PERSON PERSON

1272 Pages
1 (widescreen) slide

1272 Pages
OK, 209 pages

stole
from
loves
loves
enemy
enemy
A Good
Man Goes
to War
appeared
in
appeared
in
appeared
in
appeared
in
Victory of
the Daleks
appeared
in
appeared
in
companion
companion
enemy

stole
from
loves
loves
enemy
enemy
A Good
Man Goes
to War
appeared
in
appeared
in
appeared
in
appeared
in
Victory of
the Daleks
appeared
in
appeared
in
companion
companion
enemy
planet
prop
species
species
species
character
character
character
episode
episode

22
Modelling tip: use the Robinson* Algorithm
1. Write out the questions you
want to ask
2. Highlight/underline the nouns
3. Those are your nodes!
* Popularised by Mark Needham
http://www.markhneedham.com/blog/2013/11/29/neo4j-what-is-a-node/ @ianSrobinson

enemy
Victory of
the Daleks
appeared
in
appeared
in
companion
species character
character
episode

© 2022 Neo4j, Inc. All rights reserved. 25
25
Everything is around us is
Naturally Connected

26
Higher Pay and More Promotions
• People Near Structural Holes
• Organizational Misfits
Network Structure is
Highly Predictive
Photo by Helena Lopes on Unsplash
“Organizational Misfits and the Origins of Brokerage in Intrafirm Networks” A. Kleinbaum
“Structural Holes and Good Ideas” R. Burt

27
Relationships
are the strongest
predictors of behavior
But You Can’t Analyse
What You Can’t See
● Most data science techniques
ignore relationships
● It’s painful to manually engineer
connected features from tabular
data
● Graphs are built on
relationships, so…
● You don’t have to guess at
the correlations: with graphs,
relationships are built in
James Fowler

28
“Increasingly we’re learning
that you can make better
predictions about people by
getting all the information from
their friend and their friends’
friends than you can from the
information you have about the
person themselves.”
– Dr. James Fowler

Static vs. Connected Data
A Paradigm Shift in How to Think About Data

The Neo4j Graph Data Platform
Runs Anywhere
Deploy as-a-Service (AuraDB) or
self-hosted within your cloud of
choice (AWS, GCP, Azure) via their
marketplace, or on-premises.
Development Tools &
Frameworks
Tooling, APIs, query builder,
multi-language support for
development, admin, modeling,
and rapid prototyping needs.
Data Science and Analytics
Explorative tools, rich algorithm library,
and Integrated supervised Machine
Learning framework.
Native Graph Database
The foundation of the Neo4j platform;
delivers enterprise-scale and
performance, security, and data
integrity for transaction and analytical
workloads.
Graph Query Language
Cypher & openCypher; Ongoing
leadership and standards work (GQL)
to establish lingua franca for graphs.
Discovery & Visualization
Code-free querying, data modeling and
exploration tools for data scientists,
developers, and analysts.
Ecosystem & Integrations
Rich ecosystem of tech and
integration partners. Ingestion tools
(JDBC, Kafka, Spark, BI Tools, etc.) for
bulk and streaming needs.
30

31
NoSQL
RDBMS
• No connections
◦ Because heritage is
shopping baskets
• Documents, Columns are
rich, but stand in isolation
• Faking graph traversals via
indexes is expensive
• RDBMS is high fidelity
◦ But complex schema
operations
◦ And complex
denormalizations for
performance
• “Join bomb” problem
Established Data Models Hide the Problem

32
30 Billion
1.2 Trillion!
128 Billion
Neo4j Fabric: Scaling Up & Scaling Out!

What about
analytics?

What’s
Unusual?
What’s
Important?
What’s
Next?
Graph Data Science Helps
Make Sense of Your Data Relationships
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Exploring the hidden patterns and features in your data

35
Graphs Contain Implicit Knowledge
Which of the colored
nodes would be
considered the
most ‘important' ?

36
Graph Algorithms Help Unlock This Knowledge
D
D has the highest valence
This is the most connected individual in the
network. If importance is how well you are
personally known you pick D.
G has the highest closeness centrality (0.52)
Information will disperse through the network
more quickly through this individual. If you need
to get a message out rapidly, choose them.
G
I has the highest betweenness centrality (0.59)
This person is an efficient connector of other people.
Risk of network disruption is higher if you lose this
individual.
I
Most Important?

37
The Domains of Graph Data Science
Graph Native
Machine Learning
Learn features in your graph
that you don’t even know are
important yet using
embeddings.
Predict links, labels, and
missing data with in-graph
supervised ML models.
Identify associations,
anomalies, and trends using
unsupervised machine
learning.
Graph Algorithms
Knowledge Graphs
Find the patterns you’re looking
for in connected data

38
The easiest graph
data science
platform
Easy to use
●Automated MLOps
●Runs Anywhere
●Cloud Commitments
Built by data
scientists, for data
scientists
Data Scientists
● Native Python Client
● 65+ Graph Algos
● ML Pipelines
Go to production
with speed, security,
and scale
Enterprise Ready
● 8M Objects/sec Load
● RBAC Security
● Designed to Scale
Fits into your data
stack and pipeline
Ecosystem
●BI tools, Apache Spark,
Kafka, Data Warehouses
●Vertex AI, SageMaker,
Synapse
Neo4j Graph Data Science

The Neo4j Native Graph Catalog
• Automates data transformations
• Experiment with different data
sets, data models
• Fast iterations & layering
• Production ready features,
parallelization & enterprise
support
• Ability to persist and version
trained models
A graph-specific analytics workspace that’s mutable – integrated with Neo4j’s
native-graph database
Mutable In-Memory
Workspace
Graph Projection
Native Graph Store

40
The Largest Catalog of Graph Algorithms
Pathfinding &
Search
Centrality &
Importance
Community
Detection
Supervised
Machine Learning
Heuristic Link
Prediction
Similarity Graph
Embeddings
…and more
Over 65 Pretuned, Parallelized Algorithms

From Chaos to
Structure:
Neo4j Graph Data
Science is Changing
How Machine Learning
Gets Done
Graph Embeddings summarize the enhanced
explicit knowledge of a graph
41

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“50% of Gartner inquiries on the topic
of AI involve discussion of the use of
graph technology.”
Top 10 Tech Trends in Data and Analytics 2021
AI Research Papers
Featuring Graph
Source: Dimensions Knowledge System

43
Real World Data, Real World Fraud Detection
• Real, anonymized customer data set
• Using Neo4j Graph Data Science
• Blog post with sample code available
https://neo4j.com/developer-blog/exploring-fraud-detection-neo4j-graph-data-science-summary/
Exploring Fraud Detection With Neo4j &
Graph Data Science
Zach Blumenfeld
Data Science Product Specialist, Neo4j
87%
More fraud risks
detected!

44
Logistics and Supply Chain
Plan maritime routes based on distances, costs,
and internal logic.
Results:
● Subsecond maritime routes planning
● Reduce global carbon emissions 60,000 tons
● 12-16M ROI for OrbitMI customers

New! Native Graph Data Science
Python Client
45
● Simplifies workflows for data scientists
● Run Graph Data Science algorithms just
like any Python function
● Eliminates the need for transaction
functions for data science
● Pythonic features support for graph and
model objects

New! Explore Graph Algorithms Directly in Bloom
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New!
Graph Data Science
As-a-Service
● Handles all DB
management for you
● Simple to scale up or down
● Paused instance to save
● Starts at $1/hour
Enterprise ready, with fully
managed infrastructure,
updates, and security patches

Looking to the
future

49
“We are drowning
in information
but starved for
knowledge.”
John Naisbitt
Megatrends

50
The Knowledge Lake Architecture
Knowledge Lake
Operational Data Stores

51
jim.webber@neo4j.com
Questions?

GraphSummit Toronto: Keynote - Innovating with Graphs

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