Are You Underestimating the Value Within Your Data? A conversation about graph technology

© 2022 Neo4j, Inc. All rights reserved.
1
Are You Underestimating the Value Within Your Data?
A conversation about graph technology
Dr Jesús Barrasa
Head of Field Engineering, Neo4j
Dr Jim Webber
Chief Scientist, Neo4j

2
What’s a graph?

3
This is a chart

4
This is a graph

Relationships are the
strongest predictor of
behavior
5
But you can’t analyze
what you can’t see
• Legacy data processing
techniques ignore
relationships
• Graphs are built on
relationships, so…
• You don’t have to guess at the
correlations: with graphs,
relationships are built in

6
Labeled Property Graph Model
Nodes
• Can have Labels to classify nodes
Relationships
• Relate nodes by type and direction
• Jim likes soccer, soccer does not like
Jim
Properties
• Stored as name/value pairs
Performance
• Traversals are always O(1)
• Query latency depends on how
much of the graph you want to
explore
• It does not depend on data set size

7
Graphs are everywhere
Networks
Transport,
power,
telecoms,
data, social
Knowledge
MDM,
Customer 360,
Pedagogy,
sciences
Personalization
Recommendations,
Disambiguation,
patient/customer
journey
Discovery
IT management,
Drug discovery,
Process innovation,
Digital Twin
Decision Analysis
Cybersec,
Fraud, Policing,
Intelligence,
forecasting

8

9
Why are graphs everywhere right
now?

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Why now? A timeline
2009 2011 2015 2022

I’ve got lots of data, how do I use
analytics to get insights?
11

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Enhanced Analytics with Graph Data Science
Graph Data Science
BI & VISUALIZATIONS
DHW
STORE
PROCESS
MACHINE LEARNING
Cloud
Functions
Neo4j
Bloom
DataProc
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Business
Applications &
Existing Systems
Files (unstructured,
structured)
TensorFlow
KNIME Python
Cloud Storage
AWS Lambda

13
Graphs Contain Implicit Knowledge
Which of the colored
nodes would be
considered the
most ‘important' ?

14
Graph Algorithms Help Unlock This Knowledge
D
D has the highest valence
This is the most connected individual in the
network. If being well-known is important, pick D.
G has the highest closeness centrality (0.52)
Information will disperse through the network
more quickly through this individual. If you need
to get a message out rapidly, choose them.
G
I has the highest betweenness centrality (0.59)
This person is an efficient connector of other people.
Risk of network disruption is higher if you lose this
individual.
I
Most Important?

Better Predictions with Data You Already Have
● Traditional ML ignores network structure because it’s difficult to extract
● Graph features enrich existing ML pipelines to increase accuracy, or
● Graphs use relationships to unlock otherwise unattainable predictions
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Machine Learning Pipeline

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Machine Learning Pipelines
Graph Native ML Pipelines:
● Node Classification
● Node Regression (new!)
● Link Prediction
Trained Models & the Model Catalog:
● Save, persist & publish trained models
● Instantly apply for prediction on new and
existing data in the graph
Guided & Automated Pipeline
Features:
● Data splitting & rebalancing
● Feature engineering
● Model evaluation and
selection
● Automated hyperparameter
tuning (new!)
What we do : Link
Prediction Example
Define a pipeline, add the steps you want to perform, and we handle the rest for you.

17
“By 2025,
Graph technologies will be used in 80%
of data and analytics innovations,
up from 10% in 2021 ”
Market Guide for Graph Database Management Systems,
August 2022
Gartner

This seems very interesting, but
how do real businesses get
started?
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Leading Customer Use Cases for Graph Data Science
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Recommendations
• Identified journey archetypes and patterns
• Revealed journey similarities over time
• Found influential touch-points across
journeys
Fraud Detection
• 300%+ increase in fraud detection
• 10% True positive alert escalations (industry is <1%)
• ~150% increase in payment flow
• Reduced overall number of alert escalations
Customer 360
• 1621% increase touchpoint length
• 500% increase visits per profile
• 20-30% improvement of customer
understanding
Logistics / SC
• Subsecond maritime routes planning
• Reduce global carbon emissions 60,000 tons
• 12-16M ROI for OrbitMI customers

The CIO’s path
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Educate yourself
Graphs are here, you
need to be up to date
Prepare your
organization
Build amazing
systems
Your IT staff are already
tinkering with graph tech,
cultivate that enthusiasm
Engage with
communities of
practice, and vendors
to map out your graph
play

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Thanks for listening and…
Dr Jesús Barrasa
jesus.barrasa@neo4j.com
Dr Jim Webber
jim.webber@neo4j.com

22
… let’s continue the conversation
at the Neo4j booth
915 D&A Marketplace
Dr Jesús Barrasa
jesus.barrasa@neo4j.com
Dr Jim Webber
jim.webber@neo4j.com

Are You Underestimating the Value Within Your Data? A conversation about graph technology

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