Speaker: Fawad Zakariya, SVP for Business and Corporate Development, Neo4j

1. 1
Welcome to Graph Tour -
Toronto
Fawad Zakariya
Senior Vice President
Business & Corporate Development
Neo4j, Inc.

2. • Changing the World
• The Rise of Connections
• Introduction to Neo4j
• The World’s Leading Graph Platform
• Customer Use Cases
2
Agenda

3. Business Problem
• Find relationships between people, accounts,
shell companies and offshore accounts
• Journalists are non-technical
• Biggest “Snowden-Style” document leak ever;
11.5 million documents, 2.6TB of data
Solution and Benefits
• Pulitzer Prize winning investigation resulted in
robust coverage of fraud and corruption
• PM of Iceland & Pakistan resigned, exposed
Putin, Prime Ministers, gangsters, celebrities
(Messi)
• Led to assassination of journalist in Malta
Background
• International Consortium of Investigative
Journalists (ICIJ), small team of data journalists
• International investigative team specializing in
cross-border crime, corruption and accountability
of power
• Works regularly with leaks and large datasets
ICIJ Panama Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph3

4. ACCOUNT
HAS
REGISTE
RED
ADDRESS
PERSON
IS_OFFICER
_OF
PERSON
NAME
STREET
BANK
WITH
LIVES_
AT
LIVES_
AT
NAME
COMPANY
BANK
BAHAMAS

5. Bank
US
Account
Person
A
Company
Bank
Bahamas
Address
HA
S
REGISTERED
IS_OFFICER_
OF
WITH
LIVES_AT
LIVES_AT
NODE
RELATIONSHIP
Person
B

6. ICIJ Pulitzer Prize Winner 2017

7. Business Problem
• Find relationships between people, corporations,
accounts, shell companies and offshore accounts
• Journalists are non-technical
• 2017 Leak from Appleby tax sheltering law firm
matched 13.4 million account records with public
business registrations data from across Caribbean
Solution and Benefits
• Exposed tax sheltering practices of Apple, Nike
• Revealed hidden connections among politicians
and nations, like Wilbur Ross & Putin’s son in law
• Triggered government tax evasion investigations in
US, UK, Europe, India, Australia, Bermuda, Canada
and Cayman Islands within 2 days.
Background
• International Consortium of Investigative
Journalists (ICIJ), Pulitzer Prize winning journalists
• Fourth blockbuster investigation using Neo4j to
reveal connections in text-based, and account-
based data leaked from offshore law firms and
government records about the “1% Elite”
• Appends Neo4j-based, “Offshore Leaks Database”
ICIJ Paradise Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph7

9. “Lessons Learned Database”
A half-century of collective NASA engineering
knowledge
“How do we make sure the command
module doesn’t tip over and sink?”

10. Let’s Hear a Few Stories
— David Meza, Chief Knowledge
Architect at NASA
“Neo4j saved well over two
years of work and one million
dollars of taxpayer funds.”
Impact

11. The Rise of Connections
1

12. 1
Major Forces in a Data-Driven World
2x growth in Data every 3 years
Data Volumes1
People, Processes, Assets, Devices
are Increasingly Related
Rise in Connectedness in Data
2

13. The Rise of Connections in Data
Networks of People
Know
s
Knows
Knows
Knows
Business Processes
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
Data connections are increasing as rapidly as data volumes

14. Connectedness Represented in Graphs
C
C
A AA
U
S S SS S
USER_ACCESS
CONTROLLED_BY
SUBSCRIBED _BY
User
Customers
Accounts
Subscriptions
VP
Staff Staff StaffStaff
DirectorStaffDirector
Manager Manager Manager Manager
Fiber
Link
Fiber
Link
Fiber
Link
Ocean
Cable
Switch Switch
Router Router
Service
Organizational
Hierarchy
Product
Subscriptions
Network
Operations
Social
Networks

15. 15
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
Connected Data at the Center
AI & Machine
Learning
Price optimization
Product Recommendations
Resource allocation
Digital Transformation Megatrends

16. CONSUMER
DATA
PRODUCT
DATA
PAYMENT
DATA
SOCIAL
DATA
SUPPLIER
DATA
The next wave of competitive advantage will be all about
using connections to identify and build knowledge
Graphs Break Data Silos in The Age of Connections

17. Graph is the Fastest-Growing Database Category

18. Introduction to Neo4j
8

19. 2010 2011 2012 2013 2015 2017
Invented Cypher -
Leading language
for graph queries
First open source GA
version of a property
graph database
O’Reilly Graph
Database —
first definitive
book for graph
professionals
Introduced
labels to
simplify graph
modeling
openCypher Project
— open sourced
Cypher to create the
de facto standard
Launched
industry’s
first Graph
Platform
Neo4j — The Graph Technology Pioneer
2014
Visual Graph
Query Browser
2016
Causal
Consistency for
Graphs

20. “Neo4j continues to
dominate the graph
database market.”
69% of enterprises plan to implement graph
databases within the next 12 months.
Noel Yuhanna
Forrester Market Overview:
Graph Database Vendors
October 2017
Graph Market Acceleration

21. Neo4j is the Most Popular Graph Database
*CosmosDB score includes its primary & original use as a document database

22. Adoption Highlights
Retail
7 out of top 10
retailers in the world
Finance
12 out of 25 top
financial services firms
8 out of top 10
software vendors
Software
(As per 2017)

23. Software
Financial
Services Telecom
Retail &
Consumer Goods
Media &
Entertainment Other Industries
Airbus

24. Software
Financial
Services Telecom
Retail &
Consumer Goods
Media &
Entertainment Other Industries
Airbus
Over 250 Enterprises and 10s of
Thousands of Projects on Neo4j

25. 10M+
Downloads
3M+ from Neo4j Distribution
7M+ from Docker
Events
400+
Approximate Number of
Neo4j Events per Year
50k+
Meetups
Number of Meetup
Members Globally
Largest pool of graph technologists
50k+
Trained/certified Neo4j
professionals
Trained Developers

26. The World’s Leading Graph
Platform
6

27. Real-Time
Storage & Retrieval
Traditional Databases
Store and Retrieve Data
Confidential - Neo4j, Inc.
Today’s Big Data Technology
Aggregates & Filters Data
Long-Running Queries
Aggregation & Filtering
Neo4j Reveals
Connections in your Data
Real-Time
Connected Insights

28. How Neo4j Fits — Common Architecture Patterns
From Disparate Silos
To Cross-Silo Connections
From Tabular Data
To Connected Data
From Data Lake Analytics
to Real-Time Operations

29. 1 2 3
4 5 6
Graph Architectural Innovations create Competitive Advantage

30. Index-free adjacency ensures lightning-
fast retrieval of data and relationships
Native Graph Architecture Advantage
Index free adjacency
Unlike other database models Neo4j
connects data as it is stored

31. Neo4j Performs 10-1000x Better
10-1000x slower (log scale)

32. Connectedness Differentiates Neo4j
Conceive
Code
Compute
Store
Non-Native Graph DBNative Graph DB RDBMS
Optimized for graph workloads

33. The Neo4j Graph Platform
Development &
Administration
Analytics
Tooling
BUSINESS USERS
DEVELOPERS
ADMINS Graph
Transactions
Data Integration
Discovery & Visualization
DATA
ANALYSTS
DATA
SCIENTISTS
Drivers & APIs
APPLICATIONS
AI
openCypherCloud
Confidential – Neo4j, Inc.

34. Who We Are: The Graph Platform
Neo4j is an enterprise-grade native graph platform that enables you to:
• Store, reveal and query data relationships
• Traverse and analyze any levels of depth in real-time
• Add context and connect new data on the fly
• Performance
• ACID Transactions
• Agility
• Graph Algorithms
3
4
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Global Scale
• Graph Adoption

35. Customer Use Cases
5

36. 36
Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management
Common Graph Technology Use Cases
AirBnb

37. 37
• 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
• Replaced Oracle database
• 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

38. Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods
DISCRETE ANALYSIS

39. Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods
DISCRETE ANALYSIS Unable to detect
• Fraud rings
• Fake IP addresses
• Hijacked devices
• Synthetic Identities
• Stolen Identities
• And more…
Weaknesses

40. Entity Linking
Analysis of
relationships to detect
organized crime and
collusion
5.
Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Augment Methods by Examining Connected Data
DISCRETE ANALYSIS CONNECTED ANALYSIS
Cross Channel
Analysis of anomaly
behavior correlated
across channels
4.

41. ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
PHONE
NUMBER
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
Modeling Fraud Transactions as an Organized Ring
At first glance, each
account holder
looks normal.
Each has multiple
accounts…

42. ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 3
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE
NUMBER
PHONE
NUMBER
SSN 2
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
ACCOUNT
HOLDER 1
Modeling Fraud Transactions as an Organized Ring
AHA!
But they share
common phone
numbers, addresses
and SSNs!
These are difficult
to detect using
traditional
methods
CREDIT
CARD

43. Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay ShopBot ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations43
EE Customer since 2016 Q3

44. Case Study: Knowledge Graphs at eBay

45. Case Study: Knowledge Graphs at eBay

46. Case Study: Knowledge Graphs at eBay

47. Case Study: Knowledge Graphs at eBay

48. Bags
Case Study: Knowledge Graphs at eBay

49. Men’s Backpack
Handbag
Case Study: Knowledge Graphs at eBay

50. https://shopbot.ebay.com/
Try it out at:
Case Study: Knowledge Graphs at eBay

51. 51
The Most Powerful Database We Know

52. 52
A Highly Connected Future

53. Thank You & Make Some
Connections!
53