GraphSummit Toronto: Leveraging Graphs for AI and ML

© 2022 Neo4j, Inc. All rights reserved.
1
Phani Dathar, PhD
Data Science Solution Architect, Neo4j
Graph Data Science:
The Secret
Ingredient for
Relationship-Driven
AI

Neo4j, Inc. All rights reserved 2022
Networks of People Transaction Networks
Bought
B
ou
gh
t
V
i
e
w
e
d
R
e
t
u
r
n
e
d
Bought
Knowledge Networks
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
Risk management,
Supply chain, Orders,
Payments, etc.
Employees, Customers,
Suppliers, Partners,
Influencers, etc.
Enterprise content,
Domain specific content,
eCommerce content, etc
K
n
o
w
s
Knows
Knows
K
n
o
w
s
2
Everything is Naturally Connected

Relationship-Driven AI
● Traditional ML ignore network structure because it’s difficult to extract
● Use the right data structures to store and retrieve relationships
● Add relationships to AI/ML pipelines to make them contextual and to
unlock otherwise unattainable predictions
3
Machine Learning Pipeline

4
Let’s Start with an Example…

5
Graph to Tabular Data for ML..
Lamp Lightbulbs Pillow **HE Light bulbs
Mingo X
Jane X X
Aditi X
Fabien
…
…

6
What’s the Problem?
Generating personalized recommendations is hard due to high
dimensionality and sparse data sets
● History for every customer to generate personalized recommendations
○ Increases the problem of **sparse** and insufficient information
● Reduce dimensionality by matrix factorization and content (word)
embeddings
○ Only suited for content based recommendations
● Macro level insights for cold start problem
○ Generates poor recommendations

Take advantage of
● graphs to capture the
network structure
● graph queries to store
and retrieve
relationships
● graph algorithms to
infer relationships
What Should We Do?

Knowledge Graphs Graph Feature
Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
8
Graphs Enrich all Stages of AI Ecosystem

9
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

Predictive
Maintenance
Churn
Prediction
Fraud
Detection
Life Sciences
Personalized
Recommendations
Cybersecurity
Disambiguation &
Segmentation
Search &
Master Data Mgmt.
Graphs Data Science Applications

11
Graph Data Science
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning
Find the patterns you’re
looking for in connected data
Use unsupervised machine
learning techniques to
identify associations.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train supervise ML models
to predict links, labels, and
missing data.

12
Node
Represents an entity in the graph
Relationship
Connect nodes to each other
Property
Describes a node or relationship:
e.g. name, age, weight etc
Knowledge Graph - Building Blocks
MICA
ANDRE
Name: “Andre”
Born: May 29, 1970
Twitter: “@dan”
Name: “Mica”
Born: Dec 5, 1975
CAR
Brand “Volvo”
Model: “V70”
Since:
Jan 10, 2011
LOVES
LOVES
LOVES
LIVES WITH
O
W
N
S
D
R
I
V
E
S

13
Database, Query Language and Visualization
DATA GRAPH
QUERIES
GRAPH
VISUALIZATION
CYPHER
https://neo4j.com/developer/cypher/

What can you do with a Knowledge Graph?
Collaborative filtering: users who
bought X, also bought Y (open-
ended pattern matching)
What items make you more likely to
buy additional items in subsequent
transactions?
Traverse hierarchies - what items
are similar 4+ hops out?
How many flagged accounts
are in the applicant’s
network 4+ hops out?
How many login / account
variables in common?
Add these metrics to your
approval process
What completes the
connections from genes to
diseases to targets?
What genes can be reached
4+ hops out from a known drug
target?
What mechanisms in common
are there between two drugs?
Financial Domain Life Sciences Marketing and
Recommendations

Supply Chain: Organizational Knowledge Graph
VENDORS AND
SUPPLIERS
OPERATIONS LOGISTICS
SALES &
MARKETING
Bill Of Materials Supply Chain Customer 360

16
Graph Data Science
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
missing data.

17
Graph Algorithms
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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What are Graph Algorithms?

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

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

Personalized Recommendations
Graph algorithms and graph embeddings are used for generating
product recommendations and improving search relevance
Capture customer
interactions and customer
journey using a knowledge
graph
Analyze customer
interactions using graph
queries and find
customer communities
based on common
purchase behavior
Construct node
embeddings and
resolve entities based
on weighted pairwise
similarity between
various entities
Generate product
recommendations
based on
correlations
between products,
search queries and
historical purchases

22
Graph Data Science
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning
Use graph features to learn
the features in your graph
that you don’t even know are
important yet.
missing data.

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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Graph Machine Learning
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
The Only Completely In-Graph, ML Workflow

25
In-Graph Machine Learning
Node
classification:
“What kind of
node is this?”
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, algorithms
(pageRank), embedding

Neo4j Graph Data Science Framework
Neo4j Graph Data
Science Library
Neo4j
Database
Neo4j
Bloom
Scalable Graph Algorithms &
Analytics Workspace
Native Graph Creation &
Persistence
Visual Graph
Exploration & Prototyping

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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

28
Thank you!

GraphSummit Toronto: Leveraging Graphs for AI and ML

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