Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j 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. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Phani Dathar, PhD
Data Science Solution Architect, Neo4j
Leveraging
Graphs for AI
and ML
Relationship-Driven AI

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Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
2
Graphs enrich all stages of AI ecosystem

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3
Let’s start with an example…

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Graph to Tabular data for ML..
Lamp Lightbulbs Designer
Pillow
**HE Light bulbs
Mingo X
Jane X X
Aditi X
Fabien
…
…

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Information without context!!
Lamp Lightbulbs Designer
Pillow
**HE Light
bulbs
Mingo X
Jane X X
Aditi X
Fabien
…
…
Interior
Designers
Subsequent
Purchases
Similar
Products

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Take advantage of
● graphs to capture the
network structure
● graph queries to store and
retrieve relationships
● graph algorithms to infer
relationships and derive
context
What can (or should) we do?

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Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
7
Graphs enrich all stages of AI ecosystem

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Knowledge graphs

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Contextual recommendations with
Knowledge graphs
RECOMMENDER SYSTEM PIPELINE
Context

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Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
10
Graphs enrich all stages of AI ecosystem

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What are Graph Algorithms?

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Graph Algorithms in Neo4j GDS
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
• K-Means
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Speaker Listener Label Propagation
Supervised
Machine Learning
• Node Classification
• Link Prediction
• Node Regression
… and more!
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
• GraphSAGE
• Synthetic Graph Generation
• Scale Properties
• Collapse Paths
• One Hot Encoding
• Split Relationships
• Graph Export
• Pregel API (write your own algos)

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Graph Feature Engineering
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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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Enriched Knowledge Graphs
Structured
Unstructured
Ontologies
Graph Algorithms and
Graph Queries
Semantics,
Derived relationships and
additional context
Natural
relationships

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Enrich ML workflows
Python Client
Built for Data
Scientists
● Native Python Client
● 65+ Pretuned Algos
● Single API for load,
analysis & writeback
● Production tier
graduation

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Graph algorithms and graph embeddings are used for generating
context and resolving identities/entities
Identity Management / Entity Resolution
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

17. Neo4j, Inc. All rights reserved 2022
Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
17
Graphs enrich all stages of AI ecosystem

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Queries
Find the patterns you know exist.
Machine Learning
Uncover trends and make
predictions
Visualization
Explore, collaborate, and explain
Graph Data Science
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries
Machine Learning Visualization

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Neo4j Bloom
Explore Graphs Visually
Prototype Concepts Faster
Collaborate Across Teams

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Ad-hoc Exploration Workflows
Search
Refine
Look for
something
Visualize
Layout
Style
View results
Select
Filter
Identify subset
Expand
Pathfind
Cluster
Aggregate
Edit
Annotate
Act
Take an action
Save &
Share
Collaborate
Logical graph
view
Non-functional
considerations
UI / UX
considerations

21. Neo4j, Inc. All rights reserved 2022
Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
21
Graphs enrich all stages of AI ecosystem

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Neo4j is part of your data ecosystem
DATA SOURCES USE CASES
INGEST
Apache
Hop
Structured
Unstructured
DATA
ANALYTICS
DATA
MANAGEMENT
Journey Analytics
Risk Analytics
Churn Analysis
What-if Analysis
Feature
Engineering & ML
Fraud
Recommendations
Data Fabric
Data Compliance
Data Governance
Data Provenance
Data Lineage
Next Best Case
Ontologies
Neo4j
Bloom
Neo4j
GDS Library
PRODUCT COMPONENTS
APOC
VISUALIZE
AUTO ML
DRIVERS & APIs

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Integrated architecture for Contextual ML predictions
Data Pipeline BI & Analytics
ML & Data Science Development
ML Deployment & Monitoring
Users
Web Apps
Services
Jobs
Feature Engineering & Knowledge
Graph
Data Source
Neo4j
Database
Neo4j GDS
in-memory
projections
Neo4j Graph
Data Science
BI Connector
Bloom Graph
Visualization
Code
Registry
Batch Inference
Snowflake
ETL
Neo4j DWH
Connector
Vertex AI Neo4j GDS Python
Recommendation Service
Cloud
Storage
Cloud
Scheduler
Cloud
Composer
TensorFlow
TensorFlow
Cypher

24. Neo4j, Inc. All rights reserved 2022
Neo4j, Inc. All rights reserved 2022
Thank You
Phani Dathar, PhD
Data Science Solution Architect, Neo4j
https://www.linkedin.com/in/gopi-dathar
Email: Phani.Dathar@Neo4j.com