Using Connected Data and Graph Technology to Enhance Machine Learning and Artificial Intelligence

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Using Connected Data and Graph
Technology for Better Machine Learning
(ML) and Artificial Intelligence (AI)
Cynthia Femano
Senior Solutions Architect, Neo4j
cynthia.femano@neo4j.com

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The World Is Connected,
So Is Our Data.

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As Valuable as
the Data Itself
Connections
Enhance Context

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Networks of People Transaction Networks Knowledge Networks
Risk management,
Supply chain, Orders,
Payments, etc.
Employees, Customers,
Suppliers, Partners,
Influencers, etc.
Enterprise content,
Domain specific content,
eCommerce content, etc
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Everything is Naturally Connected

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It’s Who You Know

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It’s Who You Know And Where They Are

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Higher Pay and More Promotions
• People Near Structural Holes
• Organizational Misfits
Network Structure is
Highly Predictive
“Organizational Misfits and the Origins of Brokerage in Intrafirm Networks” A. Kleinbaum
“Structural Holes and Good Ideas” R. Burt

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Relationships and
network structures
are highly predictive
and underutilized
– and already in your data.
Graphs are a natural way to
store and use this predictive
information, but different
than what you’re doing today.
Productionize more accurate,
predictive models

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© 2022 Neo4j, Inc. All rights reserved.
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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

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10 Top 10 Tech Trends in Data and Analytics, 16 Feb 2021
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

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Improving Analytics, ML & AI Across Industries

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Graphs & Data Science
Graph Analytics
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.

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When do I need Graph Algorithms?
Query (e.g. Cypher/Python)
Real-time, local decisioning
and pattern matching
Graph Algorithms
Global analysis and iterations
You know what you’re looking for
and making a decision
You’re learning the overall structure of
a network, updating data, and
predicting
Local Patterns Global Computation

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Better Predictions with Graphs
Using the Data You Already Have
• Current data science models ignore network structure
• Graphs add highly predictive features to ML models, increasing accuracy
• Otherwise unattainable predictions based on relationships
Machine Learning Pipeline
14

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Graphs & Supervised Machine Learning
Traditional ML problems where
relationships between your data points are
important predictive features
Predictions influenced by
graph structure
Predictions about
graph structure
Enhance your graph by predicting missing
data or changes to your graph that will
occur in the future

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Gene and Target Data
Graph Feature Engineering – Drug Discovery

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

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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
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Neo4j Graph Data Science 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

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Graph Data Science 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
• 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)

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Using an In-Graph, ML Workflow
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

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“Finding relationships in combinations of diverse data, using
graph techniques at scale, will form the foundation of
modern data and analytics.”
Top 10 Trends in Data and Analytics, 11 May 2020
“By 2025, graph technologies will be used in 80% of data
and analytics innovations...”
Catch the Wave!
Don’t overlook key insights present in your data

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Make Graph a 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
Neo4j
APOC
VISUALIZE
AUTO ML
DRIVERS & APIs

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Thank you!
Contact us at
sales@neo4j.com