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Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
1
Relationships Matter:
Using Connected Data for Better Machine Learning
Alicia Frame, PhD
Director of Product Management, Neo4j
Stuart Laurie
Senior Solutions Architect, Neo4j
Neo4j, Inc. All rights reserved 2021
20 of the top 25 financial firms
7 of the top 10 retailers
7 of the top 10 software vendors
Neo4j: The Graph Company
Neo4j is the creator of:
• The world’s leading graph database
• The first graph data science platform
• The most flexible graph data model
• The easiest-to-use graph query language
Thousands of Organizations Use Neo4j
2
Silicon Valley
London
Munich
Paris
Malmö
Neo4j, Inc. All rights reserved 2021
3
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
Wait, what’s a graph?
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
Neo4j, Inc. All rights reserved 2021
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
4
Everything is Naturally Connected
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
5
Relationships
are the strongest
predictors of behavior
But You Can’t Analyse
What You Can’t See
● Most data science techniques
ignore relationships
● It’s painful to manually engineer
connected features from tabular
data
● Graphs are built on
relationships, so…
● You don’t have to guess at the
correlations: with graphs,
relationships are built in
James Fowler
Neo4j, Inc. All rights reserved 2021
6
6 Top 10 Tech Trends in Data and Analytics, 16 Feb 2021
According to Garner, “Graphs form
the foundation of modern D&A,
with capabilities to enhance and
improve user collaboration, ML models
and explainable AI.
The recent Gartner AI in Organizations
Survey demonstrates that graph
techniques are increasingly
prevalent as AI maturity grows,
going from 13% adoption when AI
maturity is lowest to 48% when
maturity is highest.”
AI Research Papers
Featuring Graph
Source: Dimensions Knowledge System
4x
Increase in
traffic to
Neo4j GDS
page in
2H-2020
Analytics & Data Science Interest
Exploding in Neo4j Community
+4.8m
Views on
the graph
algorithms
short video
+193k
downloads
Neo4j, Inc. All rights reserved 2021
7
Queries
Find the patterns you know exist.
Machine Learning
Uncover trends and make
predictions
Visualization
Explore, collaborate, and explain
Graphs & Data Science
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries
Machine Learning Visualization
Neo4j, Inc. All rights reserved 2021
8
Graphs & 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,
anomalies, and trends.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train in-graph supervise ML
models to predict links,
labels, and missing data.
Neo4j, Inc. All rights reserved 2021
Neo4j’s 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
Neo4j, Inc. All rights reserved 2021
Robust Graph Algorithms & ML methods
● Compute metrics about the topology and connectivity
● Build predictive models to enhance your graph
● Highly parallelized and scale to 10’s of billions of nodes
10
The Neo4j GDS Library
Mutable In-Memory
Workspace
Computational Graph
Native Graph Store
Efficient & Flexible Analytics Workspace
● Automatically reshapes transactional graphs into
an in-memory analytics graph
● Optimized for global traversals and aggregation
● Create workflows and layer algorithms
● Store and manage predictive models in the
model catalog
Neo4j, Inc. All rights reserved 2021
11
55+ Graph Data Science Techniques in Neo4j
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
• 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
… 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
• FastRPExtended
• GraphSAGE
• Synthetic Graph Generation
• Scale Properties
• Collapse Paths
• One Hot Encoding
• Split Relationships
• Graph Export
• Pregel API (write your own algos)
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
12
What’s New?
Neo4j, Inc. All rights reserved 2021
13
GDS 1.6: GA May 27
Compatible with Neo4j 4.x series:
• Seven algorithms graduated
to the fully supported product
tier
• All ML models now support
model persistence
• Major improvements to our
embeddings
• New capabilities like graph
filtering, scalers, and more
● Article Rank, Eigenvector
Centrality, Degree Centrality
● Pathfinding
Product tier
algos
● Project subgraphs based on
existing properties in the
in-memory graph
Subgraph
Projections
● Up to 3 models in CE
● Model persistence for node
classification, link prediction
Machine
Learning
● Improvements to node
classification, link prediction
● Scaling & normalization
ML maturity
● New algorithms for influence
maximization thanks to
@xkitsios
Community
Contributions
Neo4j, Inc. All rights reserved 2021
Machine Learning Improvements
Community Edition users now have up to 3 trained models 🎉
…. But that’s not all:
• We’d added gds.alpha.scaleProperties
, supporting min-max, max, mean,
log, standard score, L1 and L2 Norm scaling for properties
• NodeClassification and LinkPrediction now support stream and write
modes, and their models can be saved, published and restored
• Node2Vec has been promoted to the beta tier - significantly faster,
supports weights, seeding, and mutate mode
Neo4j, Inc. All rights reserved 2021
Subgraph Projections
You can now create a new in-memory graph by filtering based on properties in
your existing one with gds.beta.graph.subgraph:
• Use native projections and subset your graph,
instead of using expensive cypher projections
• Pre-process your data for faster execution, for
example calculating degree centrality and removing
high/low degree nodes, or running WCC and
creating graphs for each component
• Chain algorithms together by filtering on
properties, like running Louvain and then
calculating nodeSimilarity for each community
node.class = 1
Degree > 1
Louvain Community ID = 4
Neo4j, Inc. All rights reserved 2021
Influence Maximization Algorithms
Finding the nodes in a graph that can trigger
cascading changes:
• Who do I market to, to drive the most adoption?
• Which blogs should I read to get news first?
• Who should you test to get early warning of an outbreak?
… or: Given a network with n nodes and given a “spreading” or propagation process
on that network, choose a “seed set” s, of size k<n to maximize the number of nodes
in the network that are ultimately influenced
Neo4j, Inc. All rights reserved 2021
Influence Maximization Algorithms
Finding the nodes in a graph that can trigger
cascading changes:
• Who do I market to, to drive the most adoption?
• Which blogs should I read to get news first?
• Who should you test to get early warning of an outbreak?
This is a combinatorial optimization problem - computationally complex!
● Greedy method: polynomial time approximation
● CELF method: faster than greedy on realistic network sizes and structures
Neo4j, Inc. All rights reserved 2021
Influence Maximization Algorithms
Finding the nodes in a graph that can trigger
cascading changes:
• Who do I market to, to drive the most adoption?
• Which blogs should I read to get news first?
• Who should you test to get early warning of an outbreak?
These algorithms were contributed by community member @xkitsios 💕
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
Real World Use Cases
19
Neo4j, Inc. All rights reserved 2021
20
Accelerate Innovation using Neo4j Graph Data Science
From Simple to Highly Sophisticated Data Science
Uranus is the third
biggest planet
R&D: Better health
outcomes through
machine learning on
patient journeys
Disambiguation
with graph
algorithms at scale
Analytics to improve reliability
by predicting problems in a
supply-chain knowledge graph
Analysis Repeatability
Analysis
Complexity
Full Production
Simple, Ad Hoc
High
Analytics
Data Science
Neo4j, Inc. All rights reserved 2021
21
• Challenge: Difficulty finding at-fault
components via ad hoc analytics on a
vertically integrated supply chain
• Solution: Uses a knowledge graph to model
and analyze their complex products
• Results:
○ Quickly pinpoint root causes of
problems
○ Reduced query times from two
minutes to seconds
○ Anti-recommendation using graph
algorithms to identify and eliminate
bad combinations of components
Boston Scientific
Finding At-Fault Components
Neo4j, Inc. All rights reserved 2021
22
• Challenge: It’s hard to make
recommendations to anonymous users
• Solution: Connect first and third party
cookies using graph algorithms to create
unique profiles
• Results:
○ Converted 14B anonymous data
points into 163M user profiles
○ Drove 612% increase in web
traffic
Meredith Corp
Identifying the Anonymous
Neo4j, Inc. All rights reserved 2021
23
AstraZeneca
Patient Journey
“We used graph algorithms to find
patients that had specific journey
types and patterns and then find
others that are close and similar.”
Joseph Roemer
Global Commercial IT Insight & Analytics Sr. Director
AstraZeneca
● Challenge: How to best intervene sooner for
complex diseases that develop over years
● Solution: Neo4j knowledge graph of 3 yrs of
visits, tests, & diagnosis with 10’s Bn of
records. Using graph algorithms and
machine learning together.
● Results:
○ Identified journey archetypes and
patterns using graph feature
engineering as input to ML
○ Revealed journey similarities over
time with community detection
○ Found influential touch-points in the
journey using graph algorithms
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
Demo
24
Neo4j, Inc. All rights reserved 2021
25
Graph-Native ML Workflows inside Neo4j
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
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
26
Resources
Graph Resources
● Video: Advantages of Graph Technology
● Whitepaper: AI & Graph Technology: Enhancing AI with Context &
Connections
● Whitepaper: Financial Fraud Detection with Graph Data Science
● Case Study: Meredith Corporation
Neo4j BookShelf
● Graph Databases For Dummies
● Graph Data Science For Dummies
● O’Reilly Graph Algorithms
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
27
Resources
Get Started
● Sandbox: https://neo4j.com/sandbox/
● Guides: neo4j.com/developer/graph-data-science/
● GitHub: github.com/neo4j/graph-data-science
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
28
Thank you!
Contact us at
sales@neo4j.com

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Relationships Matter: Using Connected Data for Better Machine Learning

  • 1. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 1 Relationships Matter: Using Connected Data for Better Machine Learning Alicia Frame, PhD Director of Product Management, Neo4j Stuart Laurie Senior Solutions Architect, Neo4j
  • 2. Neo4j, Inc. All rights reserved 2021 20 of the top 25 financial firms 7 of the top 10 retailers 7 of the top 10 software vendors Neo4j: The Graph Company Neo4j is the creator of: • The world’s leading graph database • The first graph data science platform • The most flexible graph data model • The easiest-to-use graph query language Thousands of Organizations Use Neo4j 2 Silicon Valley London Munich Paris Malmö
  • 3. Neo4j, Inc. All rights reserved 2021 3 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 Wait, what’s a graph? 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
  • 4. Neo4j, Inc. All rights reserved 2021 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 4 Everything is Naturally Connected
  • 5. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 5 Relationships are the strongest predictors of behavior But You Can’t Analyse What You Can’t See ● Most data science techniques ignore relationships ● It’s painful to manually engineer connected features from tabular data ● Graphs are built on relationships, so… ● You don’t have to guess at the correlations: with graphs, relationships are built in James Fowler
  • 6. Neo4j, Inc. All rights reserved 2021 6 6 Top 10 Tech Trends in Data and Analytics, 16 Feb 2021 According to Garner, “Graphs form the foundation of modern D&A, with capabilities to enhance and improve user collaboration, ML models and explainable AI. The recent Gartner AI in Organizations Survey demonstrates that graph techniques are increasingly prevalent as AI maturity grows, going from 13% adoption when AI maturity is lowest to 48% when maturity is highest.” AI Research Papers Featuring Graph Source: Dimensions Knowledge System 4x Increase in traffic to Neo4j GDS page in 2H-2020 Analytics & Data Science Interest Exploding in Neo4j Community +4.8m Views on the graph algorithms short video +193k downloads
  • 7. Neo4j, Inc. All rights reserved 2021 7 Queries Find the patterns you know exist. Machine Learning Uncover trends and make predictions Visualization Explore, collaborate, and explain Graphs & Data Science Analytics Feature Engineering Data Exploration Graph Data Science Queries Machine Learning Visualization
  • 8. Neo4j, Inc. All rights reserved 2021 8 Graphs & 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, anomalies, and trends. Use embeddings to learn the features in your graph that you don’t even know are important yet. Train in-graph supervise ML models to predict links, labels, and missing data.
  • 9. Neo4j, Inc. All rights reserved 2021 Neo4j’s 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
  • 10. Neo4j, Inc. All rights reserved 2021 Robust Graph Algorithms & ML methods ● Compute metrics about the topology and connectivity ● Build predictive models to enhance your graph ● Highly parallelized and scale to 10’s of billions of nodes 10 The Neo4j GDS Library Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace ● Automatically reshapes transactional graphs into an in-memory analytics graph ● Optimized for global traversals and aggregation ● Create workflows and layer algorithms ● Store and manage predictive models in the model catalog
  • 11. Neo4j, Inc. All rights reserved 2021 11 55+ Graph Data Science Techniques in Neo4j 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 • 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 … 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 • FastRPExtended • GraphSAGE • Synthetic Graph Generation • Scale Properties • Collapse Paths • One Hot Encoding • Split Relationships • Graph Export • Pregel API (write your own algos)
  • 12. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 12 What’s New?
  • 13. Neo4j, Inc. All rights reserved 2021 13 GDS 1.6: GA May 27 Compatible with Neo4j 4.x series: • Seven algorithms graduated to the fully supported product tier • All ML models now support model persistence • Major improvements to our embeddings • New capabilities like graph filtering, scalers, and more ● Article Rank, Eigenvector Centrality, Degree Centrality ● Pathfinding Product tier algos ● Project subgraphs based on existing properties in the in-memory graph Subgraph Projections ● Up to 3 models in CE ● Model persistence for node classification, link prediction Machine Learning ● Improvements to node classification, link prediction ● Scaling & normalization ML maturity ● New algorithms for influence maximization thanks to @xkitsios Community Contributions
  • 14. Neo4j, Inc. All rights reserved 2021 Machine Learning Improvements Community Edition users now have up to 3 trained models 🎉 …. But that’s not all: • We’d added gds.alpha.scaleProperties , supporting min-max, max, mean, log, standard score, L1 and L2 Norm scaling for properties • NodeClassification and LinkPrediction now support stream and write modes, and their models can be saved, published and restored • Node2Vec has been promoted to the beta tier - significantly faster, supports weights, seeding, and mutate mode
  • 15. Neo4j, Inc. All rights reserved 2021 Subgraph Projections You can now create a new in-memory graph by filtering based on properties in your existing one with gds.beta.graph.subgraph: • Use native projections and subset your graph, instead of using expensive cypher projections • Pre-process your data for faster execution, for example calculating degree centrality and removing high/low degree nodes, or running WCC and creating graphs for each component • Chain algorithms together by filtering on properties, like running Louvain and then calculating nodeSimilarity for each community node.class = 1 Degree > 1 Louvain Community ID = 4
  • 16. Neo4j, Inc. All rights reserved 2021 Influence Maximization Algorithms Finding the nodes in a graph that can trigger cascading changes: • Who do I market to, to drive the most adoption? • Which blogs should I read to get news first? • Who should you test to get early warning of an outbreak? … or: Given a network with n nodes and given a “spreading” or propagation process on that network, choose a “seed set” s, of size k<n to maximize the number of nodes in the network that are ultimately influenced
  • 17. Neo4j, Inc. All rights reserved 2021 Influence Maximization Algorithms Finding the nodes in a graph that can trigger cascading changes: • Who do I market to, to drive the most adoption? • Which blogs should I read to get news first? • Who should you test to get early warning of an outbreak? This is a combinatorial optimization problem - computationally complex! ● Greedy method: polynomial time approximation ● CELF method: faster than greedy on realistic network sizes and structures
  • 18. Neo4j, Inc. All rights reserved 2021 Influence Maximization Algorithms Finding the nodes in a graph that can trigger cascading changes: • Who do I market to, to drive the most adoption? • Which blogs should I read to get news first? • Who should you test to get early warning of an outbreak? These algorithms were contributed by community member @xkitsios 💕
  • 19. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 Real World Use Cases 19
  • 20. Neo4j, Inc. All rights reserved 2021 20 Accelerate Innovation using Neo4j Graph Data Science From Simple to Highly Sophisticated Data Science Uranus is the third biggest planet R&D: Better health outcomes through machine learning on patient journeys Disambiguation with graph algorithms at scale Analytics to improve reliability by predicting problems in a supply-chain knowledge graph Analysis Repeatability Analysis Complexity Full Production Simple, Ad Hoc High Analytics Data Science
  • 21. Neo4j, Inc. All rights reserved 2021 21 • Challenge: Difficulty finding at-fault components via ad hoc analytics on a vertically integrated supply chain • Solution: Uses a knowledge graph to model and analyze their complex products • Results: ○ Quickly pinpoint root causes of problems ○ Reduced query times from two minutes to seconds ○ Anti-recommendation using graph algorithms to identify and eliminate bad combinations of components Boston Scientific Finding At-Fault Components
  • 22. Neo4j, Inc. All rights reserved 2021 22 • Challenge: It’s hard to make recommendations to anonymous users • Solution: Connect first and third party cookies using graph algorithms to create unique profiles • Results: ○ Converted 14B anonymous data points into 163M user profiles ○ Drove 612% increase in web traffic Meredith Corp Identifying the Anonymous
  • 23. Neo4j, Inc. All rights reserved 2021 23 AstraZeneca Patient Journey “We used graph algorithms to find patients that had specific journey types and patterns and then find others that are close and similar.” Joseph Roemer Global Commercial IT Insight & Analytics Sr. Director AstraZeneca ● Challenge: How to best intervene sooner for complex diseases that develop over years ● Solution: Neo4j knowledge graph of 3 yrs of visits, tests, & diagnosis with 10’s Bn of records. Using graph algorithms and machine learning together. ● Results: ○ Identified journey archetypes and patterns using graph feature engineering as input to ML ○ Revealed journey similarities over time with community detection ○ Found influential touch-points in the journey using graph algorithms
  • 24. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 Demo 24
  • 25. Neo4j, Inc. All rights reserved 2021 25 Graph-Native ML Workflows inside Neo4j 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
  • 26. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 26 Resources Graph Resources ● Video: Advantages of Graph Technology ● Whitepaper: AI & Graph Technology: Enhancing AI with Context & Connections ● Whitepaper: Financial Fraud Detection with Graph Data Science ● Case Study: Meredith Corporation Neo4j BookShelf ● Graph Databases For Dummies ● Graph Data Science For Dummies ● O’Reilly Graph Algorithms
  • 27. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 27 Resources Get Started ● Sandbox: https://neo4j.com/sandbox/ ● Guides: neo4j.com/developer/graph-data-science/ ● GitHub: github.com/neo4j/graph-data-science
  • 28. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 28 Thank you! Contact us at sales@neo4j.com