The document discusses the role of graph machine learning in addressing data complexity challenges faced by organizations, emphasizing the advantages of using graph databases over traditional relational and document databases. It showcases Neo4j's capabilities in building applications and analytics that facilitate data connectivity and insights through an intuitive graph structure, with a focus on its machine learning pipeline and deployment strategies. The document concludes with a call to explore new advancements in graph data science through workshops and further engagement.

1. © 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
Graph Machine Learning
in Production with Neo4
Dr. Jesús Barrasa
RVP Solutions Architecture EMEA

2. © 2023 Neo4j, Inc. All rights reserved.
2
At the heart of every enterprise challenge
is an explosion of data complexity
and organisations that treat their data
right will have a competitive advantage

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3
TODAY’S REALITY
Connections
in data are as
valuable as the
data itself

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Legacy
systems
can’t keep up
Relational Databases
don’t handle relationships well
Document Databases
don’t handle relationships at all

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A fundamentally new approach is needed
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Data, meet Graph.
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Graph creates a
more intuitive and
connected view of
data relationships,
unlocking deeper
insights and context
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THE PROPERTY GRAPH: SIMPLY POWERFUL
Employee City
Company
Nodes represent
objects (nouns)
Relationships are directional
Relationships connect nodes are
represent actions (verbs)
Relationships can have properties
(name/value pairs)
Nodes can have
properties (name/value
pairs)
name: Amy Peters
date_of_birth: 1984-03-01
employee_ID: 1
:HAS_CEO
start_date: 2008-01-20
:LOCATED_IN
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NEO4J: FOR APPLICATIONS AND ANALYTICS
Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions

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APPLICATIONS AND ANALYTICS (IN PRACTICE)
APP
Applications APP APP
Databases
Servers
3 primary + 2 secondary 3 primary
Composite Database
Secondary Only (GDS)
“Replica for analytical work”
ML Pipeline
Primary
“Transactional database”

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APPLICATIONS AND ANALYTICS (IN PRACTICE)
The neo4j platform can take care of “everything” (closed loop)
But you can also use your own etl tool, feature store, model catalogue,
auto ml ….
gds.alpha.graph.construct(
'items_graph',
nodes,
relationships
)
That is why we provide a python client with arrows support … … and connectors

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SEAMLESS FOR ALL ECOSYSTEMS AND PIPELINES
Graph Data Science
BI & VISUALIZATIONS
INGEST
STORE
PROCESS
Apache
Kafka
MACHINE LEARNING
Cloud
Functions
Neo4j
Bloom
PubSub
DataProc
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Business
Applications &
Existing Systems
Files (unstructured,
structured)
TensorFlow
KNIME Python
Cloud Storage
AWS
Lambda
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Graph Database

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GRAPH DATA SCIENCE
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Machine Learning Pipeline
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
K
n
o
w
s
Knows
Knows
K
n
o
w
s
Explore the hidden patterns and features in your data
What’s important? What’s unusual? What’s next?

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

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PATH TO GRAPH DATA SCIENCE
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
→
→

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# Neo4j ML Pipeline
pipeline, _ = gds.beta.pipeline.nodeClassification.create("provider-fraud-pipe")
pipeline.addNodeProperty('fastRP', embeddingDimension=64, mutateProperty='embedding',
relationshipWeightProperty='weight', iterationWeights=[0.0, 1.0, 1.0, 0.7])
pipeline.configureSplit(testFraction=0.3, validationFolds=5)
pipeline.selectFeatures(['embedding'])
pipeline.addLogisticRegression(tolerance=0.00001, maxEpochs=500, penalty=0.0, batchSize=32)
pipeline.addMLP()
pipeline.addRandomForest(maxDepth=20)
pipeline.configureAutoTuning(maxTrials=100)
trained_pipe_model, res = pipeline.train(g_train,
modelName="fraud-model",
targetNodeLabels=['Provider'], targetProperty="fraud_label",
metrics=["ACCURACY", "F1_WEIGHTED"])
result = trained_pipe_model.predict_write(g_train, concurrency=8,
writeProperty="predicted_label",
predictedProbabilityProperty="predicted_probablity")
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Most well connected and influential nodes
Recommendations from shared user interactions and
associations
VISUAL AND EXPLAINABLE RESULTS
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VISUAL AND EXPLAINABLE RESULTS
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What’s in it for you:
● Improve model accuracy
● Simplify processes = remove
headaches
● More projects into production
without oversized teams
GRAPH DATA SCIENCE
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries & Search
Machine Learning Visualization

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FLEXIBLE CLOUD DEPLOYMENT MODELS
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Graph-as-a-Service Cloud Managed Services Self-hosted
Fully-managed SaaS
Consumption-based pricing
Cloud-native
Self-service deployment
No access to underlying
infrastructure and systems
White-glove managed service
by Neo4j experts
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud
For private and hybrid
cloud, or on-prem
Bring your own license
Full control of your environment
Run in any cloud, in your account

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WHAT NEXT?
13:30 Workshop (W6): GPT and Graph
Data Science to power your Knowledge
Graph with Kristof Neys
Come talk to us at booth C68

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“Together we make sense of data”
Thank you