Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science

1. © 2022 Neo4j, Inc. All rights reserved.
What’s New With Graph Data Science
We’ve come a long way!
Alicia Frame, PhD,
Sr. Director of Data Science - Neo4j

2. © 2022 Neo4j, Inc. All rights reserved.
How it started…

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How it’s going…
18 more algorithms
Graph EmbeddingS
In-Graph Supervised ML
So much Faster!
New Enterprise Features
ML Ops Support
Improved Compression

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4
Deployment
01
Capabilities
The Basics What is graph data science?
What is Neo4j Graph Data
Science , and what’s it do?
How - and where - can I run
graph data science?
02
03
New Stuff
Ecosystem
How does Graph Data Science
fit in with my workflows?
What’s the latest and greatest?
04
05
Overview

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5
Your data is meaningless without relationships
It’s not the raw numbers, it’s the relationships behind them
Plants
Warehouses
Suppliers
Distributors
Competitors
Partners
Regulations
Employees
Citizens
Customers
Products
Parts
Services
Regions

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Use your relationships to answer questions -
that you couldn’t answer without graph
Queries
Find the patterns you know exist.
Machine Learning
Graph Algorithms, Feature
Engineering, and Predictions
Visualization
Explore, collaborate, and explain
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries
Machine Learning Visualization

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Doing Graph Data Science
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning
Use unsupervised machine
learning techniques to
identify associations,
anomalies, and trends.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train in-graph supervise ML
models to predict links,
labels, and missing data.
Find the patterns you’re
looking for in connected data

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Who is most important? What’s the best choice? What will happen next?
8
Connections answer questions
Who has the most connections? Who else bought the same item? What’s the most common path?

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Who is most important? What’s the best choice? What will happen next?
9
Connections answer questions
Who has the most connections?
Who has the highest page rank?
Who else bought the same item?
What’s the shortest path?
What’s the most common path?
Who is in the same community?

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Who is most important? What’s the best choice? What will happen next?
10
Connections answer questions
Who has the most connections?
Who has the highest page rank?
Who is an influencer?
Who else bought the same item?
What’s the shortest path?
What is the most likely purchase?
What’s the most common path?
Who is in the same community?
What relationship will form?

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Connections answer questions
Personalized
Recommendations
Churn
Prediction
Market
Segmentation
Life
Sciences
Logistics &
Supply Chain
Cybersecurity
Master Data
Management
Fraud
Detection

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Neo4j Graph Data Science
How does Neo4j help you leverage relationships to
answer business critical questions?

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© 2022 Neo4j, Inc. All rights reserved.
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Answer your Questions with
Neo4j Graph Data Science
Data Sources
Data Science and Analytics Tools
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.
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 (AuraDS) or self-hosted within your cloud of
choice (AWS, GCP, Azure) via their marketplace, or on-premises.
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

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© 2022 Neo4j, Inc. All rights reserved.
14
Answer your Questions with
Neo4j Graph Data Science
Data Science and Analytics Tools
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.
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 (AuraDS) or self-hosted within your cloud of
choice (AWS, GCP, Azure) via their marketplace, or on-premises.
Neo4j Graph Data
Science Library
Scalable Graph Algorithms
Analytics Workspace
Neo4j
Bloom
Visual Graph
Exploration & Prototyping
Neo4j
Database
Native Graph Creation &
Persistence

15. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
15
Answer your Questions with
Neo4j Graph Data Science
Data Science and Analytics Tools
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.
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 (AuraDS) or self-hosted within your cloud of
choice (AWS, GCP, Azure) via their marketplace, or on-premises.
Graph Algorithms
Unsupervised ML to find
patterns & anomalies
ML Pipelines
Supervised ML to make
graph predictions
Graph Catalog
Native Graph Creation &
Persistence
Unsupervised ML to find
patterns & anomalies

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Graph Catalog
• Single API to materialize graph
projections
• Subset, reshape and transform
your underlying database
• Modify projections with mutate
• Graph Catalog manages your
projections
• (new!) Backup, restore, or
export projected graphs
We transform your underlying data store into a highly optimized CSR
representation, built for massively parallel data science workloads.
Mutable In-Memory Workspace
Computational Graph
Native Graph Store

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Graph Catalog
• Single API to materialize graph
projections
• Subset, reshape and transform
your underlying database
• Modify projections with mutate
• Graph Catalog manages your
projections
• (new!) Backup, restore, or
export projected graphs
We transform your underlying data store into a highly optimized CSR
representation, built for massively parallel data science workloads.
Mutable In-Memory Workspace
Computational Graph
Native Graph Store
Want to learn more?
Scaling into Billions of Nodes and Relationships with
Neo4j Graph Data Science
Wednesday @ 10 am

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Graph Algorithms
Pathfinding &
Search
• Shortest Path
• Single-Source Shortest Path
• Delta Stepping
• 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
• K-means Clustering
• Leiden Algorithm
• Conductance
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• Node Similarity
• Filtered Node Similarity
• K-Nearest Neighbors (KNN)
• Filtered KNN
• Functions: Pearson, Jaccard,
Overlap, Cosine, Euclidean,
Euclidean Distance
Graph
Embeddings
• Node2Vec
• FastRP, FastRPExtended
• GraphSAGE

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Machine Learning Pipelines
AutoML for in-graph machine learning:
● Node Classification
● Node Regression (new!)
● Link Prediction
ML pipelines support:
● Data splitting & rebalancing
● Feature engineering
● Model evaluation and selection
● Automated hyperparameter tuning (new!)
Trained models in the model catalog
● Persistable
● Publishable
● Automatically applies pipelines
to new data for predictions

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20
Deployment
How, and where, can I deploy graph data science?

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Single Node, Read Scaling, or (new!) Hybrid
Neo4j Graph Data Science can be configured for your needs
Data Science Platform
● Large, single instance for built for
scale up analytics
● Configure with plenty of heap for
in-memory computation
● Graph Data Science Library
Supports
○ In-memory graph projections
○ Highly parallelized algorithms
○ Automated ML pipelines
Hybrid Deployment
● Add a specially configured GDS
node to your causal cluster
● Writeback is managed
automatically via server side
routing
● Data on the GDS node is kept in-
sync with the cluster
● Pausing updates to the GDS node
can improve performance
Read Scaling
● Connect one or more read replicas
to your primary GDS instance
● Support for:
○ Read (query or algo) scaling
○ Bloom Server
○ Disaster Recovery

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Deploy Anywhere You Want
AuraDS
Data Science as a Service
Self Managed Containerized
Deploy where you want,
customized architecture
for your use case
Spin up a transient
environment to use as a
data science tool
We manage the
infrastructure so you can
focus on data science

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Introducing
Making Graph
Data Science Easy
● Sizing guidance
● Scale on demand
● Metrics
● Migration & Cloning
Enterprise ready, with fully
managed infrastructure,
updates and security
patches

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Introducing
Making Graph
Data Science Easy
● Sizing guidance
● Scale on demand
● Metrics
● Migration & Cloning
Enterprise ready, with fully
managed infrastructure,
updates and security
patches

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25
Introducing
Making Graph
Data Science Easy
● Sizing guidance
● Scale on demand
● Metrics
● Migration & Cloning
Enterprise ready, with fully
managed infrastructure,
updates and security
patches
Want to learn more?
New! Neo4j AuraDS:
The Fastest Way to Get Started with Data Science in the Cloud
Today @ 4:45pm

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Ecosystem
Graph Data Science fits easily into your existing workflows

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Supported Connectors & Drivers
Apache Spark
Connector
Neo4j BI
Connector
Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NET
JavaScript Go

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New! Data Warehouse Connector
● We’re extending our Spark connector
infrastructure to make it easier than ever to move
data between your data warehouse and Neo4j
● Support for Snowflake, Google BigQuery,
Amazon RedShift, and Azure Synapse
● Use as a:
○ Spark Submit Job (provide JSON
configuration)
○ Simplified Scala/Python API
Data Warehouse
Connector

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New! Apache Arrow Integration
Arrow is a language-independent
columnar memory format for flat
and hierarchical data:
• Data is organized for efficient analytic
operations on CPU and GPU
• Arrow Flight is used to move Arrow
data efficiently between processes
and machines
GDS 2.1 incorporates Apache Arrow for fast graph projection (from external
sources), database creation, and exporting algorithm results

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31
Brand New: Apache Arrow Integration
Arrow is a language-independent
columnar memory format for flat
and hierarchical data:
• Data is organized for efficient analytic
operations on CPU and GPU
• Arrow Flight is used to move Arrow
data efficiently between processes
and machines
GDS 2.1 incorporates Apache Arrow for fast graph projection (from external
sources), database creation, and exporting algorithm results
Want to learn more?
Achieve Blazing Fast Ingest Speeds with Apache Arrow
Today @ 4:45pm

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32
New! Python Client
Native Python client - graphdatascience
makes it easier than ever before to use GDS
Data scientists don’t need to use (or
understand) transaction functions
- Wraps the Neo4j python driver (and newly added
dataframe support)
- Run GDS algorithms just like you would any
python function
- run_cypher lets you execute Cypher statements
Pythonic features: support for graph and
model objects

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33
New! Python Client
Native Python client - graphdatascience
makes it easier than ever before to use GDS
Data scientists don’t need to use (or
understand) transaction functions
- Wraps the Neo4j python driver (and newly added
dataframe support)
- Run GDS algorithms just like you would any
python function
- run_cypher lets you execute Cypher statements
Pythonic features: support for graph and
model objects
Want to learn more?
Integrated Graph Machine Learning with GDS 2.0 & Python
Today @ 3pm

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Putting it all together: GDS is a part of your tech stack
ML Development & Training
ML Deployment & Monitoring
Data Pipeline
Data Sources
ML Deployment ML Monitoring
Register Model
Data Enrichment
API/Service
ML Users
Web Apps
Services
Jobs
Gateway ML
API/Service
ETL
Amazon Glue
Amazon Kinesis Amazon MSK
Amazon DataSync
ML Development
Notebook Canvas
Amazon SageMaker
ML Pipeline
Data
Processing
Model
Training
Model
Evaluation
Pass Model
Evaluation
Model Status
Change Event Rule
CloudFormation SageMaker
Endpoint
CloudWatch
Metrics
CloudWatch
Alarm
(Data/Model Drift)
Trigger Model
Retraining
Approve Model/
Auto-Approve
(Model Retrianing)
Insights
Logs
Model Monitoring Job
Feature Stores
Feast
Data lake
Amazon S3
Bloom
GDS
Python
Feature Engineering
(& Data Validation)
Amazon
Lambda
Amazon EMR Neo4j
Database
GDS Graph
Catalog
Graph Data
Science

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What else is new?
We’ve already covered a lot of ground, but there’s still ✨so much more✨

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New Algorithms
Leiden Algorithm:
Like Louvain - but better (less likely to
create disconnected communities).
Hierarchical, modularity based
community detection.
K-means Clustering:
Cluster data based on properties
(like graph embeddings) - allows
users to specify the number of
communities they expect

36. © 2022 Neo4j, Inc. All rights reserved.
37
New Algorithms
Leiden Algorithm:
Like Louvain - but better (less likely to
create disconnected communities).
Hierarchical, modularity based
community detection.
K-means Clustering:
Cluster data based on properties
(like graph embeddings) - allows
users to specify the number of
communities they expect
Want to learn more?
Interpreting the Results of Community Detection Algorithms
Wednesday @ 11:30 am

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Transparency & Logging
“What is GDS doing?”
Progress Logging:

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Transparency & Logging
“What is GDS doing?”
System Monitoring: What resources are available? Who’s doing what?

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Transparency & Logging
“What is GDS doing?”
Warnings and Hints: Need to double check if you’re on the right track? Want
to know if there’s anything you’re missing? Try gds.alpha.userLog!

40. © 2022 Neo4j, Inc. All rights reserved.
New in GraphML: regression & autotuning
Node classification:
“What label should this node have?”
Link prediction:
“Should there be a relationship between
these nodes?”
Labeled data: Pairs of
nodes that are either
linked or not
Features: Pre-existing
attributes, algorithm
results, embeddings
Property Regression (new)
“What’s the value for this missing property?”
We discover the best model for you - you just supply the data!
41

41. © 2022 Neo4j, Inc. All rights reserved.
New in GraphML: regression & autotuning
Node classification:
“What label should this node have?”
Link prediction:
“Should there be a relationship between
these nodes?”
Labeled data: Pairs of
nodes that are either
linked or not
Features: Pre-existing
attributes, algorithm
results, embeddings
Property Regression (new)
“What’s the value for this missing property?”
We discover the best model for you - you just supply the data!
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Coming Soon: No Code Data Science

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Thank you!
alicia@neo4j.com
@aliciaframe1