Deeper Insights with Graph Data Science

© 2022 Neo4j, Inc. All rights reserved.
Deeper Insights
with Graph
Data Science

Phani Dathar, Ph.D.
Graph Data Science Director

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 AI Ecosystem
Neo4j Neo4j GDS Neo4j Bloom Neo4j Connectors

KNOWLEDGE GRAPHS
Knowledge graphs
provide deep,
dynamic context.
Connecting data
adds context and
improves outcomes.

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

Engineering and
Graph ML
Graph Analytics,
Investigations and
Counterfactuals
Integrations and
Knowledge Graphs
for Heuristic AI
Capitalize
Analysis
Data Modeling
GRAPH DATA SCIENCE

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.

WHAT ARE GRAPH ALGORITHMS?

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

10
WHERE ARE THESE APPLIED?
Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Identity & Access
Management
Risk &
Compliance

ENRICHED KNOWLEDGE GRAPHS
Structured
Unstructured
Ontologies
Graph algorithms and
Graph Queries
Semantics,
Derived relationships and
additional context
Natural
relationships

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

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

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

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

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

17
GRAPH DATA SCIENCE MATURITY MODEL
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
→
→

18
NEO4J GDS: IMPROVE MODELS AND ANSWER BIG QUESTIONS
Iterate fast with different data sets,
models, and version trained models.
The Largest Catalog of
Graph Algorithms
Native Graph Catalog and
Analytics Workspace
Graph Embeddings for
Machine Learning
Pathfinding
& Search
Centrality Community
Detection
Machine
Learning
Link
Prediction
Similarity Embeddings And more …
Over 65 pretuned, parallelized algorithms. Bring the DNA of your graph into a
format that other pipelines can ingest.

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 BLOOM: GRAPH VISUALIZATION

NEO4J CONNECTORS: OPERATIONALIZE GDS WORKFLOWS

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

Deeper Insights with Graph Data Science

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