Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.

1. © 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
1
B O S T O N

2. © 2023 Neo4j, Inc. All rights reserved.
2
Leveraging Graphs for
Artificial Intelligence and
Machine Learning
Phani Dathar, PhD
Director, Graph Data Science
phani.dathar@neo4j.com

3. © 2023 Neo4j, Inc. All rights reserved.
Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
Neo4j Neo4j GDS Neo4j Bloom Neo4j Connectors
GRAPHS ENRICH ALL PHASES OF AI ECOSYSTEM

4. © 2023 Neo4j, Inc. All rights reserved.
KNOWLEDGE GRAPHS
Knowledge graphs
provide deep,
dynamic context.
Connecting data
adds context and
improves outcomes.

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VENDORS AND
SUPPLIERS
OPERATIONS LOGISTICS
SALES &
MARKETING
Bill Of Materials Supply Chain Customer 360
VALUE CHAIN: ORGANIZATIONAL KNOWLEDGE GRAPH

6. © 2023 Neo4j, Inc. All rights reserved.
Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
GRAPH DATA SCIENCE
Neo4j Neo4j GDS Neo4j Bloom Neo4j Connectors

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WHAT IS GRAPH DATA SCIENCE?
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries & Search
Machine Learning Visualization
WHAT? Use context and
relationships between data points
to enhance analytics and ML
WHY? Faster, simpler, more
accurate predictions and models
whenever/wherever context
matters.

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8
KNOWLEDGE GRAPHS TO GRAPH MACHINE LEARNING
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
→
→

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WHAT ARE GRAPH ALGORITHMS?

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INSIGHTS FROM GRAPH ALGORITHMS
Outliers, Influencers, Vulnerabilities,..
Recommendations, Homophily, Outliers,..
Recommendations, What-if Analysis, Disambiguation,..
Dimensionality Reduction, Representation Learning, ..
Shortest Path, Optimal path, Route Optimization,...
Link prediction, Recommendations, Next-Best Action,..
Centrality
Pathfinding
Community
Detection
Similarity
Embeddings
Link Prediction

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IDENTITY MANAGEMENT/ ENTITY RESOLUTION
Graph algorithms and graph embeddings are used for generating
context and resolving identities/entities
Neo4j APOC
Capture relationships between
entities across data sources
using a knowledge graph
Create additional
weighted relationships
based on similar text
description and/or other
similar metadata
Construct node
embeddings and
resolve entities based
on weighted pairwise
similarity between
various entities
Identify communities
of entities based on
distance between
node embeddings

12. © 2023 Neo4j, Inc. All rights reserved.
GRAPH ENRICHED ML WORKFLOWS
Graph-Native
Feature
Engineering
Train
Predictive Model
Queries
Algorithms
Embeddings
1. Model Type
2. Property
Selection
3. Train & Test
4. Model
Selection
Apply Model to
Existing / New
Data
Use Predictions
for Decisions
Use Predictions to
Enhance
the Graph
Publish & Share
Store Model in
Database

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GRAPH FEATURE ENGINEERING
Human-crafted query, human-readable result
MATCH (p1:Person)-[:ENEMY]->(:Person)<-[:ENEMY]-(p2:PERSON)
MERGE (p1)-[:FRIEND]->(p2)
AI-learned formula, machine-readable result
Predefined formula, human-readable result
PageRank(Emil) = 13.25
PageRank(Amy) = 4.83
PageRank(Alicia) = 4.75
Node2Vec(Emil) =[5.4 5.1 2.4 4.5 3.1]
Node2Vec(Amy) =[2.8 1.8 7.2 0.9 3.0]
Node2Vec(Alicia)=[1.4 5.2 4.4 3.9 3.2]
Queries
Algorithms
Embeddings
Machine
Learning
Workflows
Train ML models
based on results

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NEO4J GDS: IMPROVE MODELS AND ANSWER BIG QUESTIONS
14
Pathfinding
& Search
Centrality Community
Detection
Machine
Learning
Link
Prediction
Similarity Embeddings And more …
Over 65 pretuned, parallelized algorithms. Iterate fast with different data sets, models,
and version trained models.
Bring the context of your connected data
into a format that other pipelines can ingest.
The Largest Catalog of
Graph Algorithms
Native Graph Catalog and
Analytics Workspace
Graph Embeddings for
Machine Learning

15. © 2023 Neo4j, Inc. All rights reserved.
Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
Graphs Enrich All Phases of Decision Making
Neo4j Neo4j GDS Neo4j Bloom Neo4j Connectors

16. © 2023 Neo4j, Inc. All rights reserved.
NEO4J BLOOM: GRAPH VISUALIZATION

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NEO4J CONNECTORS: OPERATIONALIZE GDS WORKFLOWS

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18
Demo: Claims Investigation
with Graph Data Science

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CLAIMS INVESTIGATION
Explore Claims
Data by
visualization
Generate
hypothesis /
theories
Insights from
connected data
Graph algorithms to
generate topological
features
ML models with
graph features
1
2
3
4
5

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NEO4J: FOR APPLICATIONS AND ANALYTICS
Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions