Neo4j GraphTalk Oslo 2019 Rik Van Bruggen, Neo4j

1. Welcome to the Oslo
1
Tuesday, May 28th 2019

2. Introduction
2

3. • 9:00 - 9:30 - Breakfast Networking - Welcome
• 9:30 - 12:30 - Presentations
• Introduction to the Neo4j Graph Platform
Rik Van Bruggen, Neo4j
• Exploring networks of companies to predict default probabilities
Aiko Yamashita, DNB
• Building Intelligent Solutions with Graphs
Dinuke Abeysekera, Neo4j
• 12:30 - Q&A & Networking
3
Our agenda for today

4. About Neo4j
Rik Van Bruggen
rik@neo4j.com
@rvanbruggen
4

5. 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
5
Who We Are: Leader in Graph Innovations
• Performance
• ACID Transactions
• Schema-free Agility
• Graph Algorithms
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Global Scale
• Graph Adoption
Graph
Transactions
Graph
Analytics
Data Integration
Development
& Admin
Analytics
Tooling
Drivers & APIs Discovery & Visualization

6. 720+
7/10
12/25
8/10
53K+
100+
300+
450+
Adoption
Top Retail Firms
Top Financial Firms
Top Software Vendors
Customers Partners
• Creator of the Neo4j Graph Platform
• ~250 employees
• HQ in Silicon Valley, other offices include
London, Munich, Paris and Malmö Sweden
• $80M new funding led by Morgan Stanley &
One Peak. Total $160M from Fidelity,
Sunstone, Conor, Creandum, and
Greenbridge Capital
• Over 15M+ downloads & container pulls
• 325+ enterprise subscription customers
with over half with >$1B in revenue
Ecosystem
Startups in program
Enterprise customers
Partners
Meet up members
Events per year
Industry’s Largest Dedicated Investment in Graphs
Neo4j - The Graph Company

7. Strictly ConfidentialStrictly Confidential
7
Neo4j Is Helping The World To Make Sense of Data
ICIJ used Neo4j to uncover the
world’s largest journalistic leak to
date, The Panama Papers
NASA uses Neo4j for a “Lessons
Learned” database to improve
effectiveness in search missions
in space
Neo4j is used to graph the human
body, map correlations, identify
cause & effect and search for the
cure for cancer
SAVING
DEMOCRACY
MISSION
TO MARS
CURING CANCER

8. The Neo4j Graph Platform
Rik Van Bruggen
rik@neo4j.com
@rvanbruggen
8

9. Networks of People Business Processes Knowledge Networks
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
The Rise of Connections in Data
Electronic Networks
On-prem & cloud
computing, Cellular,
Telco & Internet, IoT,
Blockchain

10. 10
Graph Databases are Designed for Connected Data
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data Connections in data
Real time storage & retrieval Real-Time Connected Insights
Long running queries
aggregation & filtering
“Our Neo4j solution is literally thousands of times
faster than the prior MySQL solution, with queries
that require 10-100 times less code”
Volker Pacher, Senior Developer
Up to
3
Max
# of
hops
1 Millions

11. CAR
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
PERSON PERSON
11

12. Collections-Focused
Multi-Model, Documents, Columns
& Simple Tables, Joins
Neo4j is designed for data relationships
Different Paradigms
NoSQL
Relational
DBMS
Neo4j Graph
Platform
Connections-Focused
Focused on
Data Relationships
Development Benefits
Easy model maintenance
Easy query
Deployment Benefits
Ultra high performance
Minimal resource usage

13. 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

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

15. Graph
Transactions
Graph
Analytics
Data Integration
Development
& Admin
Analytics
Tooling
Drivers & APIs Discovery & Visualization
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
Native Graph Platform: Tools for Many Users

16. 16
Strongly Differentiated Commercial Offering
Enterprise Edition is Highly Differentiated from CE
Full Text Indexing & Search2
✔ ✔
Date/Time data type1
✔ ✔
3D Geospatial data types1
✔ ✔
Native Indexes – up to 5x faster writes1
✔ ✔
100B+ Bulk Importer1
✔ Resumable1
Enterprise Cypher Runtime up to 70% faster – ✔
Hot Backups – 2x Faster1
ACID Transactions ✔ ✔
High-performance native API ✔ ✔
High-performance caching ✔ ✔
Cost-based query optimizer ✔ ✔
Index-backed ORDER BY sorting in Cypher2
✔ ✔
Graph algorithms library to support AI initiatives ✔ ✔
Massively parallel graph algorithms – ✔
Query monitoring with enriched metrics – ✔
User and role-based security – ✔
Brute force login prevention settings2 – ✔
LDAP and Active Directory Integration – ✔
Kerberos security option – ✔
Multi-Clustering
(partition of clusters)1 – ✔
Automatic Cache Warming1 – ✔
Rolling Upgrades1 – ✔
Resumable Copy/
Restore Cluster Member
– ✔
New diagnostic metrics
and support tools1 – ✔
Property Blacklisting – ✔
Language drivers for Java, Python, Go2, C# &
JavaScript ✔ ✔
Bolt Binary Protocol & Seabolt, C-based driver
framework2 ✔ ✔
RPM, Debian, Docker, Azure & AWS Cloud
Delivery ✔ ✔
Intra-cluster encryption secures traffic & server
comms2 across data centers & cloud zones
– ✔
IPv6 support in clustered deployments – Available
High throughput, least-connected load balancing
built into Bolt drivers
– ✔
Causal Clustering, core and read-replica design
at global scale for applications, analytics
workflows, HA and DR
– ✔
Enterprise Lock Manager accesses all cores on
server
– ✔
Labeled property graph model ✔ ✔
Native graph processing & storage ✔ ✔
Cypher graph query language ✔ ✔
Neo4j Browser with syntax highlighting ✔ ✔
Fast writes via native indexes2
✔ ✔
Composite Indexes ✔ ✔
Cypher for Apache Spark (CAPS) for big data
analytics ✔ ✔
Graph size limitations 34B nodes None
Auto reuse of deleted space – ✔
Property existence constraints – ✔
Cypher query tracing, monitoring and metrics – ✔
Node Key schema constraints – ✔
Neo4j Desktop: Free developer-friendly
package with full database and tools
– ✔
CommunityDatabase Features Architectural Features Graph Platform Features
1New in Neo4j 3.4 & 23.5
Enterprise Community Enterprise Community Enterprise

17. Neo4j Bloom
17
• High fidelity
• Scene navigation
• Property views
• Search suggestions
• Saved phrase history
• Property editor
• Schema perspectives
• Bloom chart type
• Visualize
• Communicate
• Discover
• Navigate
• Isolate
• Edit
• Share

18. Graph & ML Algorithms in Neo4j+35
neo4j.com/
graph-algorithms-
book/
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Link Prediction
Finds optimal paths
or evaluates route
availability and quality
Determines the importance
of distinct nodes in the
network
Detects group
clustering or partition
options
Evaluates how alike
nodes are
Estimates the likelihood
of nodes forming a future
relationship
Similarity

19. Graph and ML Algorithms in Neo4j
• Parallel Breadth First Search &
DFS
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• Minimum Spanning Tree
• A* Shortest Path
• Yen’s K Shortest Path
• K-Spanning Tree (MST)
• Random Walk
• Degree Centrality
• Closeness Centrality
• CC Variations: Harmonic, Dangalchev,
Wasserman & Faust
• Betweenness Centrality
• Approximate Betweenness Centrality
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity – 1 Step & Multi-
Step
• Balanced Triad (identification)
• Euclidean Distance
• Cosine Similarity
• Jaccard Similarity
• Overlap Similarity
• Pearson Similarity
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
neo4j.com/docs/
graph-algorithms/current/
Updated April 2019
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors

20. 1. Knowledge Graphs
Context for Decisions
2. Connected
Feature Extraction
Context for Credibility
4. AI Explainability3. Graph-
Accelerated AI
Context for Efficiency
Context for Accuracy
Four Pillars of Graph-Enhanced AI

21. Graph Database Surging in Popularity
21

22. 20M+
Downloads
8M+ from Neo4j Distribution
12M+ from Docker
Events
400+
Approximate Number of
Neo4j Events per Year
50k+
Meetups
Number of Meetup
Members Globally
50k+
Trained/certified Neo4j
professionals
1k Certified
Trained Developers
Largest Pool of Graph Technologists

23. "Neo4j continues to dominate the graph
database market.”
“69% of enterprises have, or are planning
to implement graphs over next 12 months”
October, 2017
“The most widely stated
reason in the survey for
selecting Neo4j was
to drive innovation”
February, 2018
Critical Capabilities for DBMA
“In fact, the rapid rise of Neo4j and other graph
technologies may signal that data
connectedness is indeed a separate paradigm
from the model consolidation happening across
the rest of the NoSQL landscape.”
March, 2018
Analysts See Unique Benefits of Graphs
"Neo4j is the clear market leader in the graph space. It has the
most users, it uses a widely adopted language that is much easier
than Gremlin and in many respects, it has consistently been a lot
more innovative than its competitors.”
“It is the Oracle or SQL Server of the graph database world.”
March, 2019
"Our research suggests that graph databases have the
best chance to survive and thrive as a distinct
category (versus the other NoSQL models) because
connected data applications present serious performance
problems that only a specialized graph DB can solve.”
March, 2019

24. Graph Use Cases
24

25. 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
Graph Use Cases by Value Proposition

26. 26
Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management
Common Graph Technology Use Cases
AirBnb

27. Software
Financial
Services Telecom
Retail &
Consumer Goods
Media &
Entertainment Other Industries
Airbus
27 Copyright © 2017 Neo4j, Inc. Company Confidential

28. Graphs Drive Innovation
28
Context Paths
Auto-Graphs
Graph Layers
1st Graph
Cross-Connect
Cross-tech applications
Internet of Things operations
Transparent Neural
Networks
Blockchain-managed
systems
Adjacent graph layers inspire
new innovations
Metadata / Risk Management
Knowledge Graphs
AI- Powered Customer
Experiences
Connect unlike objects such
as people to products,
locations
Mobile app explosion
Recommendation engines
Fraud detectors
Desire for more context to
follow connections
Connects like objects
People, computer networks,
telco, etc

29. 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 Graph29

30. ACCOUNT
ADDRESS
PERSON
PERSON
NAME
STREET
BANK
NAME
COMPANY
BANK
BAHAMAS

31. Bank
US
Account
Person
A
Company
Bank
Bahamas
AddressNODE
RELATIONSHIP
Person
B

32. ICIJ Pulitzer Price Winner 2017

33. Paradise Papers Metadata Model

34. 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.
• Granted $1M endowment from Golden Globes’ HFPA
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 Graph34

35. Neo4j Customer Deep Dive
35

36. 36
• 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

37. 3 Types of Knowledge Graphs
Internal knowledge documents
& files, with meta data tagging
External data source aggregation
mapped to entities of interest
Context Rich Search External Event Insight
Sensing
Enterprise NLP
Graph technical terms, acronyms,
abbreviations, misspellings, etc.
Examples:
• MDM, Search
• Customer support
• Document classification
Examples:
• Supply chain/compliance risk
• Market activity aggregation
• Sales opportunities
Examples:
• Improved search
• Chatbot implementation
• Improved classification
Context Independent
WarehouseReal-Time WarehouseLogical Warehouse

38. 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”
Adobe Behance Social Network of 10M Graphic Artists
Social Network38
EE Customer since 2016 Q

39. 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 for Google Assistant ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations39
EE Customer since 2016 Q3

40. Background
• Over 7M citizens suffer from Diabetes
• Connecting over 400 researchers
• Incorporates over 50 databases, 100k’s of Excel
workbooks, 30 database of biological samples
• Sought to examine disease from as many angles as
possible.
Business Problem
• Genes are connected by proteins or to metabolites,
and patients are connected with their diets, etc…
• Needed to improve the utilization of immensely
technical data
• Needed to cater to doctors and researchers with
simple navigation, communication and connections
of the graph.
Solution and Benefits
• Dr. Alexander Jarasch, Head of Bioinformatics and
Data Management
• Scientists can conduct parallel research without
asking the same questions or repeating tests
• Built views like a liver sample knowledge graph
DZD - German Center for Diabetes Research
Medical Genomic Research40
EE Customer since 2016 Q

41. Questions?
Answers!
41

42. • 9:00 - 9:30 - Breakfast Networking - Welcome
• 9:30 - 12:30 - Presentations
• Introduction to the Neo4j Graph Platform
Rik Van Bruggen, Neo4j
• Exploring networks of companies to predict default probabilities
Aiko Yamashita, DNB
• Building Intelligent Solutions with Graphs
Dinuke Abeysekera, Neo4j
• 12:30 - Q&A & Networking
42
Our agenda for today

43. Thank You
43