The Path To Success With Graph Database and Analytics

© 2023 Neo4j, Inc. All rights reserved.
The Path To Success With Graph
Database and Analytics
Dr. Alexander Jarasch
Vertical Lead - Pharma and Life Sciences EMEA

This is a plot / chart This is graph
Let continue talking about graphs

Neo4j Graph Database and
Analytics
3
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
Application Development Tools & Frameworks
Tools and APIs for rapid prototyping and development
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

4
Plugs into your data and development ecosystem
Neo4j BI
Connector
Apache Spark
Connector
Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NET
JavaScript Go

● 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
5
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

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

Developer Productivity: Rich tooling and easy onramp
7
ops manager
data importer
Visualize and explore your data
Query editor and results visualizer
Code-free data loader and modeler
NeoDash (BI)

8
Native Graph Architecture
Autonomous
Clustering
Composite
• Now with elastic
scale-out to 100s of
machines across
clusters
• Unlimited high
throughput
• Federation of scaled
out shards
• Instant composite
database
Composite DB
Maintain data integrity and high speed when scaling out
Native Graph
Storage
Native Graph
Processing
• No mismatch
• Data integrity / ACID
• Schema flexible
• 1000x faster than relational
• K-Hop now 10-1000x faster
than version 4

Graph Traversals
9
SQL (478ms) Cypher in Neo4j (1ms)
Kudos to Millie Zhou, Qiagen Digital Insights

GenAI & Semantic Search Journey
10
Find relevant documents and
content for user queries
Find entities associated to
content and patterns in
connected data.
Improve search relevance &
insights by enhancing a
Knowledge Graph. Use graph
algorithms and ML to
discover new relationships,
entities, and groups.
Vector Similarity
Search
Graph Traversals &
Pattern Matching
Knowledge Graph
Inference & ML
Vector Database
Graph Database

Hybrid Workload Duality
11
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

Powers Neo4j Graph Data Science
Graph Data Science
MACHINE LEARNING
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
TensorFlow
KNIME Python
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Project your graph for in-memory analytics
● Unparalleled analytical processing
○ with 70+ Algorithms for predictive analytics
○ and pipeline to supervised AI/ML models
● Making AI smarter!

Ontologies / Terminologies
13

14
Semantic Search / Semantic Similarity

LLMs & Neo4j
15

Knowledge Graph
Neo4j AuraDS
Graph Data
Science
Graph DB
Intelligent Apps
Knowledge
Extraction and
Ingestion
Structured
Unstructured
Ontologies
Data Sources API Layer
Customer Service
Ticket Triaging
Recommendations
News Content &
Discovery
Enterprise
Knowledge Search
Patient
Prioritization
Clinical Decision
Support Systems
Pharmacovigilance
Health Assistants
FAQ Bots
Knowledge Graph and Generative AI Reference Architecture
Bloom
Google Vertex AI
Azure OpenAI
Service
Amazon Bedrock
Google Vertex AI
Azure OpenAI
Service
Amazon Bedrock

LLMs can Jumpstart Knowledge Graph Creation
LLMs can be prompted to:
1. Extract data from unstructured text
• Entity extraction, including pronouns and
partial references
• Entity disambiguation
• Extraction of semantic relationships
1. Aide or directly perform graph ingestion
• Generate Cypher for ingesting extracted data
• Structure extracted data (triples, json, csv) to
work with Cypher insert templates
Input Data
LLM
User
Neo4j

RAG with a vector search enabled KG
LLM
Embeddings API
Vector Search
Enabled KG
LLM Chat API
User
Vector
Search
Prompt + Relevant
Information
Prompt Response
Relevant Results
/ Documents
Embedding
Retrieve relevant documents using
LLM embeddings & vector search
2
3
1
1
2
3
Combine relevant documents with
prompt
Instruct LLM to only use the relevant
documents to generate response

Text Embedding Vectors for Semantic Search
Given a question, find the most relevant documents based on a similarity metric (such as
Cosine Similarity) between vector of the question and vectors of contents.
Moving from keyword search to similarity (semantic) search.
Q: what is text
embedding?
abstractId similarity
456 0.923445
22 0.892114
… ...
Top K by similarity

20
https://neo4j.com/blog/vector-search-deeper-insights/

Why & What is a Vector Index?
● Data applied on: encoding vectors of mainly unstructured data such as text,
audio, video that is converted using embedding models (“Raw” vectors).
● Main purpose: deploy approximate methods to perform similarity search at
lower computational cost.
● Once an embedding vector has been created as a node property a vector
index can be created across those properties.
● This indexing is an algorithm that maps the original vector to a data
structure that enables faster search.
● By creating a vector index a data structure optimized for queries is created at
“store time” (as opposed to GDS similarity search at query time).

Neo4j Graph Data Science
22

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?
23
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ay
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Lives_in
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Likes
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f
Plays_for
K
n
o
w
s
Knows
Knows
K
n
o
w
s
Graph Structure Improves Data Science Outcomes

24
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
> 70+ 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

25
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.

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
26
Graph Database

View the most well connected and influential nodes
Recommendations from shared user interactions and associations
Our Visualizations Make analysis easy to understand

28
What’s in it for you:
● Improve model accuracy by 30%
● Simplify processes and remove
headaches
● More projects into production
without additional hiring
Neo4j Graph Data Science
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries & Search
Machine Learning Visualization

29
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

30
How to get started…
3. Graph Native
Machine Learning
Learn features in your graph
that you don’t even know are
important yet using
embeddings.
Predict links, labels, and
missing data with in-graph
supervised ML models.
Identify associations,
anomalies, and trends using
unsupervised machine
learning.
2. Graph Algorithms
1. Knowledge Graphs
Find the patterns you’re looking
for in connected data

31
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
New
Image courtesy of: Traag, V.A., Waltman, L. & van Eck, N.J.
Image courtesy of: javatpoint.com
Leiden Algorithm:
K-means Clustering:

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
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

35
Major Features
K-Core decomposition
- Community detection
- Social networks, drug discovery and network design
- Community request!
Bellman-Ford algorithm
- Cover use cases that have negative weights in pathfinding
- Used in routing (networks and logistics), BoM
Better high degree nodes support
- Faster loading
- Faster algorithms
Model serialization
- Now supports MLP, Random Forests, pipelines
- Needed to persist through AuraDS shutdown
2.4 Updates
• Major Features
• Minor Features
• Important Changes

36
Minor Features
2.4 Updates
• Major Features
• Minor Features
Improve ScaleProperties
Efficient Graph Sampling Method with
Common Neighbor Awareness
Arrow plugin: progress tracking for write
back
Disable progress logging when running a
high volume of queries
Shutdown in AuraDS more stable when
running algorithms

37
Important Changes
2.4 Updates
• Major Features
• Minor Features
New Context Management in Python Client
- Allow graph projection in with statements where graph
is dropped at end
- Recommended for transient patterns with graph
projections
Cypher projection 2.0
- Cypher Aggregation now considered the new standard.
No longer alpha.
- Previous Cypher Projection moving to legacy
Promotions & Breaking Changes

38
Other
Data Stores
Transactions Analytics
Graph Database Graph Data Science
Integrated AI/Machine
Learning
Data
Integrations
&
Connectors
Admin
Cypher
Drivers
&
APIs
Dev
Tools
Application Layer: Digital Twin, Recommendation, Fraud Detection, Cybersecurity, …
Query
Browser
GraphQL
Analytics & AI/Machine Learning Pipelines
Neo4j Graph Database and Analytics
Flexible Graph Schema
Performance, Reliability &
Integrity
Scale-Up & Scale-Out Architecture
Development Tools Breadth
Enterprise Ecosystem

39
Thank you!
Contact us at
sales@neo4j.com
Share feedback at slido.com
#GraphSummitFrankfurt2023

The Path To Success With Graph Database and Analytics

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