Neo4j : la voie du succès avec les bases de données de graphes et la Graph Data Science

© 2023 Neo4j, Inc. All rights reserved.
La voie du succès
avec les bases de données de graphes
et la Graph Data Science
Nicolas Rouyer
Ingénieur avant-ventes senior, Neo4j
1

Neo4j Graph
Database & Analytics
2
BUSINESS
USERS
DEVELOPERS
DATA
SCIENTISTS
DATA
ANALYSTS
Enterprise Ready
Data Science & MLOps
Graph Data Science
OLAP
Data Science and Analytics
Tools, algorithms, and Integrated ML framework
AutoML
Integrations
Discovery & Visualization
Low-code querying, data modeling and exploration tools
Neo4j
Bloom
BI
Connectors
Neo4j
Browser
Language
interfaces
Graph Query Language
Cypher and GQL as the lingua franca for graphs
Transactions Analytics
Graph Database
Data Consolidation
Contextualization
OLTP
Native Graph Database
The core component of Neo4j platform
Runs Anywhere
Run by yourself or as DBaaS by Neo4j, in the cloud or on premises
Data Connectors
Ecosystem & Integrations
Rich set of connectors to plug into existing data ecosystems
Data Sources
Application Development Tools & Frameworks
Tools and APIs for rapid prototyping and development

Engineering Expertise
>1000 person-years investment
First mover advantage
Maturity, Most enterprise deployments
Largest graph community
Growing at 80%+ annually
Neo4j Graph Database Capabilities
Hybrid
Workloads
Native Graph
Architecture
Powers
Graph Data
Science
Rich
Toolset
Enterprise
Trust
Runs
Anywhere
3

4
Native Graph Architecture
Composite DB
Data integrity and high speed
also true in scaled out situations
At write time
Data is connected
as it is stored
At read time
Lightning-fast retrieval of
data and relationships via
pointer chasing
Native Graph Storage
➔ No model mismatch
➔ Data integrity with ACID
➔ Flexible schema
Native Graph Processing
➔ 1000x faster than relational
➔ Easy-to-use, native graph query
language
➔ K-Hop now 10-1000x faster than
version 4
Autonomous Clustering
➔ Elastic scale-out for high
throughput
➔ 100s of machines across
clusters
Fabric
➔ Federation of scaled
out shards
➔ Instant composite
database

Query Performance as Connectivity Increases
5
Connectedness and Size of Data Set
Response
Time
Relational joins and
other NoSQL key-value
retrievals
1000x Advantage
Minutes to milliseconds
Many hops
> 3 degrees
Thousands of connections
0 to 2 hops
0 to 3 degrees
Few connections
5

Hybrid Workload Duality
6
Intelligent Applications
Transactions
Security
Performance & Scalability
ACID Consistency
Intelligent Modeling
Extensive & Supported Algo Library
Scalable
Graph Visualization
Graph Transformations
Graph
Transactions
Graph Analytics &
Data Science

Developer Productivity: Rich Tooling And Easy Onramp
ops manager
7
data importer
Visualize and explore your data
Query editor and results visualizer
Code-free data loader and modeler
Unified Workspace

8
Plugs Into Your Data And Development Ecosystem
Neo4j BI Connector
Apache Spark
Connector Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NET
JavaScript Go

Enterprise-Grade: Security And Trust Built In
Single Sign-On Secure Development
Practices
Dedicated
VPC
Role- & Schema-Based
Access Control
Encryption
(At-Rest, In-Transit,
and Intra Cluster)
SOC 2 Type 1
9

Real-time Performance at Scale
Automatic Upgrades, Patches, Backups
Scale on Demand, No Downtime
High Availability
Multi Cloud, Any Region
Enterprise-grade Security
Simple Capacity-Based Pricing
10
Run Anywhere: Self Managed, Or By Neo4j
Full administrative control
On-premises or via cloud marketplace
Fit where cloud isn’t appropriate (e.g. special compliance
scenarios)
Easy migration to AuraDB
Self-Managed

Forward Looking Investments
Theme: the first-choice and primary database that graph-powers any application
Performance at
Scale
Analytic step-up performance with Parallel Cypher Queries
Improved mem-to-storage ratio / Lower TCO with Freki next-gen storage
Even more autonomous clustering with declarative server management
Operational Trust Better monitoring and tuning with query analyzer in Neo4j Ops Manager
Integrated observability with AuraDB metrics and log streaming
Customer managed security in AuraDB with customer managed encryption keys
and customer-managed RBAC (Now available!)
Developer
Experience
Complete multi-cloud availability AuraDB on Azure, GCP, and AWS
Making graph ubiquitous with openCypher as a path to ISO GQL (est. 2024)
Programmatic management and monitoring with APIs for AuraDB
Solidifying Neo4j as the data store of record: CDC + next-gen Kafka connector
11

Neo4j Graph Data Science
12

What’s important?
Prioritization
Who has the most connections?
Who has the highest page rank?
Who is an influencer?
What’s unusual?
Anomaly & Fraud Detection
Where is a community forming?
What are the group dynamics?
What’s unusual about this data?
What’s next?
Predictions
What’s the most common path?
Who is in the same community?
What relationship will form?
13
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 Structure Improves Data Science Outcomes

14
With The Largest Catalog of Graph Algorithms
Pathfinding &
Search
Centrality &
Importance
Community
Detection
Supervised
Machine Learning
Heuristic Link
Prediction
Similarity Graph
Embeddings
…and more
Graph algorithms are a set of instructions that visit the nodes of a graph to
analyze the relationships in connected data.

Eliminates Complexity Optimizes Data
Science Flows
Complex joins operations
Mining Multiple Tables
Tedious Manual
Approximations
Brute Force Comparisons
Fractured Data
Reduction in
computation time
Faster than open
source libraries
Graph algorithms
and ML pipelines
Improves Customer
Outcomes
20-30% improvement in model
performance
600% improvement in traffic
$5 Million of additional
fraud detected
3x better churn predictability
5x reduction in factory
production lead time
16
x
x
x
x
x
95%
500x
+ +
Better Outcomes With The Data You Already Have
Easier And Faster
65+
16

17
Data Scientists
> Native Python Client
> Apache Arrow integration
> Unified ML pipelines
We Invest In Four Key Areas
Built by data scientists,
for data scientists
Better Predictions
> 65+ Graph algorithms &
embeddings
> Graph native ML Pipelines
> Vertex AI & SageMaker
Integrations
The best graph data
science and ML engine
Ecosystem
> Apache Spark & Kafka
Connectors
> Native BI Connector
> Data Warehouse Connector
> GNN library support
Seamlessly works with
your data stack and
pipeline
Production Ready
> Compatible with all major
clouds
> Enterprise Scale & Security
> Deploy anywhere
Go to production with
speed, scale, and
security

18
100% Graph Native Ml Lifecycle Support
Data Collection
& Preparation
Drivers, Connectors,
Fast Import/ Export
Exploratory
Data Analysis
Graph Queries, Algorithms,
and Visualization
Feature Engineering
Graph Embeddings & Algorithms
Model Training
& Tuning
Graph Native ML Pipelines
Unsupervised Graph Algorithms
Model Evaluation
& Selection
Model
Deployment
Predict APIs, Model/Graph
Catalog Operations, Connectors

And Made It Seamless For All Ecosystems And Pipelines
Graph Data Science
BI & VISUALIZATIONS
INGEST
STORE
PROCESS
Apache
Kafka
MACHINE LEARNING
Cloud
Functions
Neo4j
Bloom
PubSub
DataProc
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Business
Applications &
Existing Systems
Files (unstructured,
structured)
TensorFlow
KNIME Python
Cloud Storage
AWS
Lambda
19
Graph Database

View the most well connected and
influential nodes
Recommendations from shared user
interactions and associations
Quickly adapt to new questions
21
Graph Structure Make Analysis Easy To Understand

22
Neo4j Graph Data Science
➔ Improve outcomes by 30%
➔ Simplify processes &
remove headaches
➔ More projects into production without
additional hiring
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Query & Search
Algorithms &
Machine Learning
Graph
Visualization

23
Customer Case Study:
Fraud Detection
Correctly identify account holders committing fraud
Results:
➔ 300% increase in fraud detection
➔ 10% true positive escalations (industry
standard < 1%)
➔ Reduced false positive escalations
➔ 150% increase in payment flow

How To Get Started…
Knowledge Graphs
Graph Algorithms
Graph Native ML
Find the patterns you’re looking
for in connected data
Identify associations, anomalies,
and trends using unsupervised
machine learning
Learn features in your graph
that you don’t even know are
important yet with node
classification, regression, and
link prediction
→
→
24

25
What’s New in
Graph Data Science

Algos & Embeddings
HashGNN Embedding:
Faster approach than GNNs for knowledge graphs
KMeans:
Cluster data based on properties like
graph embeddings
Leiden Algorithm:
Fast and scalable modularity based
community detection
Image courtesy of: Traag, V.A., Waltman,
L. & van Eck, N.J.
Image courtesy of: javatpoint.com
Leiden Algorithm:
K-means Clustering:
26
New

ML Pipelines
Autotuning:
Find optimal hyperparameters to improve
model performance
Multilayer Perceptrons:
(MLPs): Fully connected neural networks
now available for Link Prediction and
Node Classification
27
New

GNN Support
Graph Sampling:
sample a representative subgraph from a
larger graph for training complex models
Graph Export:
use our projections in other graph ML
libraries like Deep Graph Library (DGL),
PyG, and Tensorflow GNN
New
Image courtesy of Google Cloud
28

Continue Your Graph Journey
Connect With Passionate Graphistas
Free online training
and certification
• dev.neo4j.com/learn
• dev.neo4j.com/datasets
Graph expert group:
The Ninjas
• dev.neo4j.com/ninjas
Connect with the community:
• dev.neo4j.com/chat
• dev.neo4j.com/forum
• dev.neo4j.com/newsletter
Next developer events
• Live Streams - Weekly & Online
• Local Meetups neo4j.com/events
29

Meet The Neo4j Ninjas: Masters Of Graphs
Ninjas are:
Active graph bloggers, presenters,
GitHub contributors, professors, user
group leaders, and researchers - all
sharing their graph expertise
Benefits:
Ninjas benefit from exclusive access to
Neo4j experts, VIP event experience,
special giveaways and much more
Interested? For more
information visit:
30

APOC Documentation
Other Neo4j Resources
Neo4j Graph Data Science Documentation
Neo4j Cypher Manual
Neo4j Driver Manual
Cypher Style Guide
Arrows App
• APOC is a great plugin to level up your
cypher
• This documentation outlines different
commands one could use
• Link to APOC documentation
• The Cypher manual can be used to get
more information about Cypher commands
• Link to cypher manual
• Neo4j Graph Data Science documentation
is a great reference to see which algorithms
to use
• Show how to use different algorithms
• Link to Graph Data Science documentation
• The driver manual provides the official
drivers that are supported by Neo4j
• Link to Neo4j driver manual
• The cypher style guide provide
recommendations for building clean, easy
to read Cypher queries
• Link to Cypher style guide
• The Arrows app allows one to design a
graph without using Cypher
• Link to Arrows app
Cypher Cheat Sheet
• This page gives quick examples of how to
write different queries within Cypher
• Link to Cypher cheat sheet
GraphGists
• GraphGists has many different use cases
and examples for specific industries
• Link to GraphGists
Neo4j Sandbox
• The Neo4j sandbox provides a quick
deployment of a Neo4j server
• It does not require a download
• Comes with example projects
• Link to Neo4j Sandbox
31

Donnez votre avis sur slido.com
#GraphSummitParis2023
M E R C I
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Neo4j : la voie du succès avec les bases de données de graphes et la Graph Data Science

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