Government GraphSummit: Keynote - Graphs in Government

© 2022 Neo4j, Inc. All rights reserved.
1
Dr. Jim Webber
Chief Scientist, Neo4j
Graphs in
Government

Neo4j, Inc. All rights reserved 2022
2

CHUM_OF
CREATED
SCARED_OF PROMOTES
LED_TO
SUCCEEDED_BY
SUCCEEDED_BY SUCCEEDED_BY SUCCEEDED_BY
DEFEATED
{year: 2017}
DEFEATED
{year: 2019}
INFLUENCED
OUTLASTED
CALLED_
REF
BROKE_LAW DODGY_AS

Overview
• Introduction to Neo4j and Graphs
• A shift in thinking
• Digitization of Government
• Neo4j Platform
• Looking to the Future

Neo4j: A Rich History of Graph Innovation
5
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
5

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
6

7
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
8
#1 Most Popular
Graph Database with Developers
200k+
Developers

9
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?

11
But first…ground rules!
This
is
a
graph
This
is
a
chart

12
You land here, at LHR
Neo4j London

13
Neo4j London

14
Neo4j London

© 2022 Neo4j, Inc. All rights reserved. 15
15
Everything is around us is
Naturally Connected

16
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

17
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

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

Digitization of
Government
19

Graph Databases in Government

21

22
Money Laundering and Tax Evasion
Billions of dollars lost every single year
Funds “travel” across a highly complex network of
parties to evade scrutiny
Requires a technology that analyzes connections in
data, in real-time as transactions are being made

23
Use Case:
Modeling Money
Laundering as a
Graph
Neo4j is used to combat
advanced money
laundering schemes.
Money laundering is all
about how funds travel
across a network of
parties. Without graph
analysis capabilities,
some of these patterns
can be impossible to
detect.

24
The Cali
Cartel Money
Laundering
Scheme

25
ICIJ Offshore Leaks

27
The Use of Connected Analysis And Graph Databases
Within FinGov
How transactions occur and how money moves between
assets, companies and people is highly connected.
Neo4j is used by major banks and governments for
asset-modeling, entitlement programs and fraud detection.
Neo4j is used to combat advanced money laundering
schemes. Graph analysis makes patterns practical to detect.

28

29
Challenges for Law Enforcement
Information is often stored in many different
databases, with no easy way of search and access it.
LE-agents need to access several different databases
to gather information on a single suspect or a location
of interest.
Huge disadvantage not to have the connections
between datapoints readily apparent

30
Use Case:
Secure MDM for Law
Enforcement
Law Enforcement
Agencies use Neo4j to
model the information
into graphs to improve
efficiency and make
direct and implicit
patterns readily
apparent in real time.
A suspect often appears in several different databases

31
Use Case:
Graphs for
Investigations
Neo4j is used by LE to
track all parts of
criminal investigations,
including witnesses,
suspects, forensic
evidence, and locations.
All related directly and
indirectly.

32
Use Case:
Identifying Fraud Rings
Organizing a fraud ring in
the real world is relatively
simple. A group of people
share their personal
information to create
synthetic identities. For
example, with just 2
individuals sharing names
and social security
numbers can create 4
different identities. This
can be discovered with
connected analysis.

33
Fraud Detection with Discrete Analysis
Find fraudsters not
fraudulent
transactions!

34
Fraud Detection with Connected Analysis

35
Graph-Augmented Fraud Detection

36

37
Challenges within Security
Intelligence officers need to extract insights from
connections within massive-scale data sets, often of
various types data
Decisions often need to be made in real-time
Disparate types of data (i.e. people, locations) requires
high flexibility
The sensitivity of the data requires complex structures of
entitlement and access between individuals and agencies.

38
Use Case:
Customs and Border
Protection
Several countries use Neo4j
for real-time and analytical
capabilities in relation to
customs and border
enforcement.
Security and intelligence is
the practice of extracting
insights from massive-scale
data, often from various
data-sources and in
real-time. Deep search is
crucial when dealing with
complex situations with
many people, such as
smuggling, trafficking, and
port-of-entry ingress/egress.
Typical Use Cases
✅ Border Control
✅ Anti-Terrorism and Security
✅ Cyber Crime

39
Example of
Connections
Between
Different
Domains

40
Security and
Law
Enforcement
Knowledge
Graph
• Flexible to change
• Scalable to many
problems
• Intuitive to understand
• Instantly responsive

41

42

43
The Graphs
in E-Gov are
Everywhere!

44
Responsibilities
are diffuse,
overlapping

45
Challenges within E-Government
Inconsistency of records
Legacy technology – expensive
and time consuming to maintain
Lack of efficiency

46
Use Case:
Citizen-Centric
Systems
Citizens interact
infrequently with
government, spend a lot
of time rekeying
information.
Secure, joined up
systems encourage
good citizenship,
reduce friction, increase
departmental
productivity
Government Services

47
Use Case:
Citizen-Centric
Systems
✅ Better Services
✅ Greater Consistency
✅ Saves Time
✅ Coordinates Efforts
✅ Better data for better
government

48
How Graphs Are Used to Improve Government Services
Neo4j and graph-based applications are used to gain better access and
availability of government services, locally and nationally.
Graph Databases negates duplication of records. Storing data as a
graph provides synergies between systems and the ability to see these
connections clearly.
Neo4j enables Governments to cross-use data from different services
(i.e. traffic permits, local taxation) in creating efficient multi-purpose
and multi-platform services.

49
The Modern Supply Chain is a Graph

Supply chain
• Challenge: BOM
management for all
components, costs, related
components across lifetime
• Solution: knowledge graph
for ordering, budgeting, and
what-ifs
• Result: ordering and
replacing parts is ~8x faster,
system more agile allowing
rapid deployment

Department of Infrastructure, Transport, Regional Development and
Communications (Australia)
Making sense of billions of GPS signals
Collating chains & journeys of arbitrary length from billions of data points

Freight Graph Data Model
• 4.9 Billion nodes
• 14.6 Billion relationships
• 350 million new GPS pings per month
https://bitre-telematics.github.io/yulo_book

Industry data sharing
• Leading economic
indicator
• Infrastructure planning
and road safety
• Industry benchmarking
and trip planning

Which building is most likely to fail?

Digital Twin for Building Assurance
Neo4J

57
Neo4j’s Technology
Platform

Relational Databases
Don’t handle relationships well
● Not well adapted to interconnected semi-structure
● Schema rigidity limits business agility
● Relationship queries require painful SQL & joins
● Practically limited to three degrees of separation
Other NoSQL Databases
Don’t handle relationships at all
● Built to scale simple data (store & retrieve)
● No language or structures for handling relationships:
joins done through the application
● Lack of ACID makes graph transactions impossible
Graph Databases
Natively store, query relationships
● Data model purpose-built for graphs
● Powerful balance of schema flexibility & structure
● Highly powerful & productive query language
● Queries run 1000x faster at scale, up to 1000+
degrees of separation
58
Can’t we already do this?
58
“We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with
queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that
was previously impossible.” --- Volker Pacher, Senior Staff Engineer

Why use a Graph Database? Native Storage and Processing
Neo4j data structures link data, allowing
millions of “joins” per second per core

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

Engineering Expertise
>1000 person-years development
investment
First Mover Advantage
Maturity, Most enterprise deployments
Largest Graph Community
Growing at 80%+ annually
Market Leading Innovation & Capabilities
Transactional
+
Analytics
Workloads
Native Graph
Architecture
+
ACID
+
Schema
Flexible
In-Memory
Graph
Processing for
Algorithms
and ML
Rich and
Powerful Tools
and
Integrations
Robust
Enterprise-
Grade Security
Runs
Anywhere
62

63
Neo4j Graph
Database
Neo4j Graph Data Platform
Neo4j Graph
Data Science

Flexible and Intuitive Modeling and Queries
Whiteboard Model = Physical Model
Flexible, Schema Optional
Cypher Query Language
Easy, Intuitive and Powerful
64
Enhanced in Neo4j 5:
- Disjunction (OR) (v:L1|L2)
- Negation (NOT) (v:!L1)
- Grouping (AND) (v: (L1&L2)|(L3&!L4))

65
Powerful Queries, e.g., Variable Length / K-Hop
Examples:
• Fraud detection. Return neighbors within k
hops of an account with risk score above 123
• Discussion board. Return all messages for
this topic from people I follow, e.g.,
◦ To find good quotes
◦ To find and manage flames
• Supply chain tracking. Return all transit
sites where this package has been through,
when error xyz has occurred
In Neo4j 5, 1000x faster than its predecessor.

66
Autonomous Clustering
Easy, Automated Horizontal Scale-Out
Elastically scale out and in, on demand, for extreme
performance, guaranteed availability with consistency
Scale clusters to 100s of machines
Unbounded Scalability To Harness All Your Data
Fabric
Federated Queries and Sharded Graphs
Query multiple business graphs
Chain queries for sophisticated real-time analysis
Hybrid cloud queries

Rich Tooling For Rapid Development
Local database for rapid dev Visualize and explore your data API-driven intelligent applications
Query editor and results visualizer
data
Importer
Code-free data loader
ops
manager
Centralized management
67

World-class Security and Compliance
Single
Sign-On
Secure
Development
Practices
Dedicated
VPC
Granular Access
Controls -
Role-Based &
Schema-Based
Encryption
At-Rest,
In-Transit,
& Intra Cluster

69
Neo4j is Part of Your Ecosystem
Apache Spark
Connector
Neo4j BI
Connector
Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NET
JavaScript Go
Apache Arrow
Integration
API

70
Neo4j Graph
Database
Neo4j Graph
Data Science

What’s Important? What’s Unusual? What’s Next?
71
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
K
n
o
w
s
Knows
Knows
K
n
o
w
s
Graph Data Science Helps
Make Sense of Your Data Relationships
Explore the hidden patterns and features in your data

72
70+ Parallel, Optimized Graph Algorithms
Pathfinding &
Search
• 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
• Breadth & Depth First Search
Centrality &
Importance
• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (Greedy, CELF)
Community
Detection
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Speaker Listener Label Propagation
• Approximate Max K-Cut
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• Node Similarity
• K-Nearest Neighbors (KNN)
• Jaccard Similarity
• Cosine Similarity
• Pearson Similarity
• Euclidean Distance
• Approximate Nearest Neighbors (ANN)
Graph
Embeddings
• Node2Vec
• FastRP
• FastRPExtended
• GraphSAGE

73
Better Predictions with Graphs
Using the Data You Already Have
• Current data science models ignore network structure
• Graphs add highly predictive features to ML models, increasing accuracy
• Otherwise unattainable predictions based on relationships
Machine Learning Pipeline

74
Data Sources
Data Connectors
Transactions Analytics
Graph Database
Data Consolidation
Contextualization
Enterprise Ready
Data Science & MLOps
Graph Data Science
Neo4j
Bloom
Neo4j
Browser
BUSINESS
USERS
DEVELOPERS
DATA
SCIENTISTS
DATA
ANALYSTS
BI
Connectors
AutoML
Integrations
Language
interfaces
OLTP
OLAP
Native Graph Database
The foundation of the Neo4j platform; delivers enterprise-scale and performance,
security, and data integrity for transaction and analytical workloads.
Data Science and Analytics
Explorative tools, rich algorithm library, and Integrated supervised Machine Learning
framework.
Application Development Tools & Frameworks
Tooling, APIs, query builder, multi-language support for development, admin, modeling,
and rapid prototyping needs.
Discovery & Visualization
Code-free querying, data modeling and exploration tools for data scientists, developers,
and analysts.
Graph Query Language Support
Cypher & openCypher; Ongoing leadership and standards work (GQL) to establish
lingua franca for graphs.
Ecosystem & Integrations
Rich ecosystem of tech and integration partners. Ingestion tools (JDBC, Kafka, Spark,
BI Tools, etc.) for bulk and streaming needs.
Runs Anywhere
Deploy as-a-Service (AuraDB) or self-hosted within your cloud of choice (AWS, GCP,
Azure) via their marketplace, or on-premises.
74

75
jim.webber@neo4j.com
Questions?

Government GraphSummit: Keynote - Graphs in Government

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Government GraphSummit: Keynote - Graphs in Government

Similaire à Government GraphSummit: Keynote - Graphs in Government (20)

Plus de Neo4j

Plus de Neo4j (20)

Dernier

Dernier (20)

Government GraphSummit: Keynote - Graphs in Government