5. Photo by Helena Lopes on Unsplash
Network Structure
is highly predictive of
pay and promotions
• People Near Structural Holes
• Organizational Misfits
“Organizational Misfits and the Origins of Brokerage in Intrafirm Networks” A. Kleinbaum
“Structural Holes and Good Ideas” R. Burt
6. Relationships and Network Structure
Strongest Predictors of Behavior & Complex Outcomes
“Research into networks reveal that,
surprisingly, the most connected
people inside a tight group within a
single industry are less valuable than
the people who span the gaps ...”
6
“…jumping from ladder to ladder is a
more effective strategy, and that lateral
or even downward moves across an
organization are more promising in the
longer run . . .”
11. Relationships
The Strongest Predictors of Behavior!
“Increasingly we're learning that you can
make better predictions about people
by getting all the information from their
friends and their friends’ friends than
you can from the information you have
about the person themselves”
James Fowler
11
12. 823
1607
2439
3765
5824
0
1000
2000
3000
4000
5000
6000
7000
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Graph Is Accelerating AI Innovation
12
AI Research Papers Featuring Graph
Data Source: Dimensions knowledge system
Graph Technology
graph neural network
graph convolutional
graph embedding
graph learning
graph attention
graph kernel
graph completion
13. 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
13
14. 14
• 27 Million warranty & service documents
parsed for text to knowledge graph
• Graph is context for AI to learn “prime
examples” and anticipate maintenance
• Improves satisfaction and equipment lifespan
• Connecting 50 research databases, 100k’s of
Excel workbooks, 30 bio-sample databases
• Bytes 4 Diabetes Award for use of a
knowledge graph, graph analytics, and AI
• Customized views for flexible research angles
• Almost 70% of credit card fraud was missed
• ~1B Nodes and +1B Relationships to analyze
• Graph analytics with queries & algorithms
help find $ millions of fraud in 1st year
Neo4j for Graph Analytics, AI and Data Science
Caterpillar’s AI Supply
Chain & Maintenance
German Center for
Diabetes Research (DZD)
Financial Fraud
Detection & Recovery Top 10
Bank
17. The Steps of Graph Data Science
Decision
Support
Graph Based
Predictions
Graph Native
Learning
17
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
Knowledge
Graphs
Graph
Analytics
18. The Steps of Graph Data Science
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
18
Graph
AnalyticsKnowledge
Graphs
Graph search
and queries
Support domain
experts
19. Knowledge Graph
Connecting the Dots has become...
19
Multiple graph layers of financial information
Includes corporate data with cross-relationships and external news
20. Knowledge Graph with Queries
Connecting the Dots
Dashboards and tools
• Credit risk
• Investment risk
• Portfolio news recommendations
• Typical analyst portfolio is 200
companies
• Custom relative weights
1 Week Snapshot:
800,000 shortest path calculations for the
ranked newsfeed. Each calculation
optimized to take approximately 10 ms.
has become...
20
21. The Steps of Graph Data Science
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
21
Knowledge
Graphs
Graph
Analytics
Graph queries &
algorithms for
offline analysis
Understanding
Structures
22. Query
(e.g. Cypher)
Fast, local decisioning
and pattern matching
Graph Algorithms
(e.g. Neo4j Algorithms Library)
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
22
23. Deceptively Simple Queries
How many flagged accounts are in the
applicant’s network 4+ hops out?
How many login / account variables
in common?
Add these metrics to your approval
process
Difficult for RDMS systems over 3 hops
Graph Analytics via Queries
Detecting Financial Fraud
Improving existing pipelines to identify fraud via heuristics
23
24. Graph Analytics via Algorithms
Generally Unsupervised
24
A subset of data science algorithms that come from network science,
Graph Algorithms enable reasoning about network structure.
Pathfinding
and Search
Centrality
(Importance)
Community
Detection
Heuristic
Link Prediction
Similarity
26. Graph Algorithms
Detecting Financial Fraud
Graph algorithms enable reasoning
about network structure
Louvain to identify communities
that frequently interact
PageRank to measure influence
and transaction volumes
Connected components
identify disjointed group
sharing identifiers
Jaccard to measure account
similarity
26
27. The Steps of Graph Data Science
Graph
Embeddings
Graph
Networks
27
Knowledge
Graphs
Graph
Analytics
Graph Feature
Engineering
Graph algorithms
& queries for
machine learning
Improve Prediction
Accuracy
28. Graph Feature Engineering
Feature Engineering is combines and processes data to create new,
more meaningful features, such as clustering or connectivity metrics.
EXTRACTION
28
Client
Betweenness
Centrality
Unique Shared
Identifiers
Weighted
Score
Known
Fraudster?
Jacob Olsen 0 1 1 No
Kaylee Roach 32 2 4 Yes
Mackenzie Burns 0 0 0 No
Kayla Knowles 192 3 4 Yes
Nicholas Jones 0 1 2 No
John Smith 0.08 2 10 YesPaySim Dataset
29. Graph Feature Engineering
Feature Engineering is combines and processes data to create new,
more meaningful features, such as clustering or connectivity metrics.
29
Client
Betweenness
Centrality
Shared
Identifiers
Weighted
PageRank
Known
Fraudster?
Jacob Olsen 0 1 1 No
Kaylee Roach 32 2 4 Yes
Mackenzie Burns 0 0 0 No
Kayla Knowles 192 3 4 Yes
Nicholas Jones 0 1 2 No
John Smith 0.08 2 10 Yes
Machine Learning on
this
To Build a Predictive Model
30. The Steps of Graph Data Science
Decision
Support
Graph Based
Predictions
Graph Native
Learning
30
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
Knowledge
Graphs
Graph
Analytics
FUTURE
31. Neo4j GDS Library
Evolving the Graph Algorithms Library for Data Scientists
• Run optimized, parallel algorithms over 10’s Billions
of nodes
• Production features like seeding for consistency
• Scalable in-memory graph model that loads in
parallel, can flexibly aggregate & reshape underlying
data models
• Simplified syntax & API with easy to understand
guides, warnings, & errors messages
• Extensive documentation with examples, tips, and
browser guides
Preview
32. for Enterprise Graph Data Science
Neo4j Graph Data
Science Library
Practical, Scalable
Graph Data Science
Native Graph
Creation & Persistence
Neo4j
Database
Graph Exploration
& Prototyping
Neo4j
Bloom
Preview
34. 34
“AI is not all about Machine
Learning.
Context, structure, and
reasoning are necessary
ingredients, and Knowledge
Graphs and Linked Data are
key technologies for this.”
Wais Bashir
Managing Editor, Onyx Advisory