I gave this presentation at DataOps 19 in Barcelona. You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.

1. AI, Machine Learning and
Graph Algorithms
Real Life Use Cases with Graph Databases Neo4j

2. MATCH
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3. Neo4j - The Graph Company
500+
7/10
12/25
8/10
53K+
100+
250+
450+
Adoption
Top Retail Firms
Top Financial Firms
Top Software Vendors
Customers Partners
• Creator of the Neo4j Graph Platform
• ~200 employees
• HQ in Silicon Valley, other offices
include London, Munich, Paris and
Malmö (Sweden)
• $160M in funding from Morgan Stanley,
Fidelity and others.
• Over 10M+ downloads,
• 250+ enterprise subscription customers
with over half with >$1B in revenue
Ecosystem
Startups in program
Enterprise customers
Partners
Meetup members
Events per year
Industry’s Largest Dedicated Investment in Graphs

4. What Is A Graph?

5. What Is A Graph?

6. What Is A Graph?

7. What Is A Graph?
• Nodes (vertices)
• Relationships (links, edges)
• Properties
• Labels

8. Neo4j — Changing the World
ICIJ used Neo4j to uncover the world’s
largest journalistic leak to date, The
Panama Papers, exposing criminals,
corruption and extensive tax evasion.
The US space agency uses Neo4j for
their “Lessons Learned” database to
connect information to improve search
ability effectiveness in space mission.
eBay uses Neo4j to enable
machine learning through
knowledge graphs powering
“conversational commerce”.
Knowledge Graph for AIFraud Detection Knowledge Graph for humans

9. The world is a graph – everything is connected
• people, places, events
• companies, markets
• countries, history, politics
• sciences, art, teaching
• technology, networks, machines,
applications, users
• software, code, dependencies,
architecture, deployments
• criminals, fraudsters and their behavior

10. • Nodes
• Represent the objects in the graph
• Can be labeled
Property Graph Model Components
Car
Person Person

11. • Nodes
• Represent the objects in the graph
• Can be labeled
• Relationships
• Relate nodes by type and direction
Property Graph Model Components
Car
DRIVES
OW
NS
LOVES
Person
LOVES
LIVES WITH
Person

12. • Nodes
• Represent the objects in the graph
• Can be labeled
• Relationships
• Relate nodes by type and direction
• Properties
• Name-value pairs that can go on nodes
and relationships.
Property Graph Model Components
Car
DRIVES
OW
NS
LOVES
Person
LOVES
LIVES WITH
Person
brand: “Mini”
model: “Cooper”
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2015

13. ● Fraud Detection
● Anti Money Laundering (AML), e-commerce Fraud,
First-Party Bank Fraud, Insurance Fraud, Link
Analysis
● Real-time analysis of data relationships is essential
to uncovering fraud rings and other sophisticated
scams before fraudsters and criminals cause lasting
damage.
● https://neo4j.com/use-cases/fraud-detection
Use Cases

14. ● Fraud Detection
● Master Data Management
● 360-Degree View of Customer, Cross Reference
Business Objects, Data Ownership, Master Data,
Organizational Hierarchies
● Organize and manage your master data with the
flexible and schema-free graph database model in
order to get real-time insights and a 360° view of
your customers.
● https://neo4j.com/use-cases/master-data-manage
ment
Use Cases

15. ● Fraud Detection
● Master Data Management
● Recommendation Engine
● Content & Media Recommendations, Graph-Aided
Search Engine, Product Recommendations,
Professional Networks, Social Recommendations
● Graph-powered recommendation engines help
companies personalize products, content and
services by leveraging a multitude of connections
in real time.
● https://neo4j.com/use-cases/real-time-recommend
ation-engine
Use Cases

16. ● Fraud Detection
● Master Data Management
● Recommendation Engine
● Knowledge Graph
● Asset Management, Cataloging, Content
Management, Inventory, Workflow Processes
● Tap into the power of graph-based search tools for
better digital asset management using the most
flexible and scalable solution on the market.
● https://neo4j.com/use-cases/knowledge-graph
Use Cases

17. ● Fraud Detection
● Master Data Management
● Recommendation Engine
● Knowledge Graph
● Network and Database
Infrastructure Monitoring
● Asset Management, Cybersecurity, Impact Analysis,
Quality-of-Service Mapping, Root Cause Analysis
● Graph databases are inherently more suitable than
RDBMS for making sense of complex
interdependencies central to managing networks
and IT infrastructure.
● https://neo4j.com/use-cases/network-and-it-opera
tions
Use Cases

18. ● Fraud Detection
● Master Data Management
● Recommendation Engine
● Knowledge Graph
● Network and Database
Infrastructure Monitoring
● Social Media and Social Network Graphs
● Community Cluster Analysis, Friend-of-Friend
Recommendations, Influencer Analysis, Sharing &
Collaboration, Social Recommendations
● Easily leverage social connections or infer
relationships based on activity when you use a
graph database to power your social network
application.
● https://neo4j.com/use-cases/social-network
Use Cases

19. ● Fraud Detection
● Master Data Management
● Recommendation Engine
● Knowledge Graph
● Network and Database
Infrastructure Monitoring
● Social Media and Social Network
Graphs
● Artificial Intelligence and
Machine Learning
● Artificial Intelligence (AI) is poised to drive the next wave of
technological disruption across nearly every industry. Just like
previous technology revolutions in web and mobile, however, there
will be winners and losers based on who harnesses this technology
for a true competitive advantage.
● https://neo4j.com/use-cases/artificial-intelligence
Use Cases

20. Neo4j Is a Database
No Size
Limit
Binary &
HTTP
Protocol
ACID
Transactions
2-4 M
ops/s
per core
Clustering
Scale & HA
Official
Drivers
Neo4j
RELIABILITY
PERFORMANCE
SCALABILITY
AVAILABILITY
INTEGRATION

21. Graph
Visualization
Developer
Workbench
Extensible
Procedures
& Functions
Cypher
Query
Language
Schema-free &
Optionally
Schema-based
Property
Graph
Model
Neo4j Is a Graph Database
Native
Graph DB
Graph
Storage
Neo4j

22. Native Graph Storage
At Write Time:
data is connected
as it is stored
“We keep the connection lines alive.”
At Read Time:
Lightning-fast retrieval of data and
relationships via pointer chasing
Graph Value is found in the
Traversals and Hops
Index-free adjacency

23. Native Graph Storage
Node
Store
Relationship
Store
Property
Store
Dynamic
Store
IN
OUT
LOOP
PK
PK
PK
PK
Table A
Table B
Table C
Table D

24. The Raft Consensus Algorithm
Equivalent to Paxos in fault-tolerance and performance.
Causal Clustering
https://raft.github.io/

25. • Node property existence
• Relationship property existence
• Unique property
• Node and combined properties
uniqueness
Schema-free or Schema-based
ACTED_IN
roles: [“Zachry”]
name: Tom Hanks
born: 1956
Person Actor
name: Hugo Weaving
born: 1960
Person Actor
title: Cloud Atlas
released: 2012
Movie
ACTED_IN
roles: [“Bill Smoke”]
title: The Matrix
released: 1999
Movie
ACTED_IN
roles: [“Agent Smith”]
name: Lana Wachowski
born: 1965
Person Director
DIRECTED
DIRECTED

26. Ann
Cypher Query Language
CREATE (:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} )
LOVES
Dan
NODE
LABEL PROPERTY
Relationship NODE
LABEL PROPERTY

27. Cypher Query Language
MATCH (:Person { name:"Dan"} ) -[:LOVES]-> ( whom )
RETURN whom
NODE Relationship NODE
?
LOVES
Dan

28. Neo4j Browser

29. Neo4j Desktop

30. Neo4j
Bloom

31. Neo4j
Bloom

32. Fun without Fuss! https://neo4j.com/lp/try-neo4j-sandbox

33. Graph Analytics
Query (e.g. Cypher/Python)
Real-time, local decisioning
and pattern matching
Graph Algorithms Libraries
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

34. Predict & Prescribe
Requires Understanding Relationships and Structures
Flow & Dynamics Interactions &
Resiliency
Propagation Pathways
Complex and Emerging Behavior

35. Source: “Systemic delay propagation in the US airport network” – Fleurquin, Ramasco, Eguiluz - https://ifisc.uib-csic.es/~jramasco/text/characterization_delays.pdf

36. Planning and Least Cost Routing

37. Bridge Points Languages
Telecom Network
Source: “Fast unfolding of communities in large networks” – Blondel, Guillaume, Lambiotte, Lefebvre - https://arxiv.org/pdf/0803.0476.pdf

38. Centrality
● PageRank
● ArticleRank
● Betweenness Centrality
● Closeness Centrality
● Harmonic Centrality
● Eigenvector Centrality
● Degree Centrality
Community Detection
● Louvain
● Label Propagation
● Connected Components
● Strongly Connected Components
● Triangle Counting / Clustering Coefficient
● Balanced Triads
Similarity
● Jaccard Similarity
● Cosine Similarity
● Pearson Similarity
● Euclidean Distance
● Overlap Similarity
Graph Algorithms
https://neo4j.com/docs/graph-algorithms
https://neo4j.com/graph-algorithms-book
Path Finding
● Minimum Weight Spanning Tree
● Shortest Path
● Single Source Shortest Path
● All Pairs Shortest Path
● A*
● Yen’s K-shortest paths
● Random Walk
Link Prediction
● Adamic Adar
● Common Neighbors
● Preferential Attachment
● Resource Allocation
● Same Community
● Total Neighbors

39. Pathfinding & Search
• Single-Source Shortest Path
○ Calculates “shortest” path between a
node and all other nodes
• All-Pairs Shortest Path
○ Finds all shortest paths between
all nodes

40. https://github.com/johnymontana/osm-routing-app

41. Centrality Algorithms

42. Centrality Algorithms

43. Similarity Algorithms
Evaluates how alike nodes are at an individual
level
Properties or attributes
•Cosine Similarity Recommendations (Movies): https://neo4j.com/graphgist/movie-recommendations-with-k-nearest-neighbors-and-cosine-similarity
•Social similarities (Interests): https://medium.com/neo4j/cosine-similarity-in-neo4j-d617b0442439

44. Community Detection Algorithms
Evaluates how a group is clustered or partitioned
Different approaches to define a community
•Label Propagation Prediction Drug-Drug Interaction: https://neo4j.com/blog/graph-algorithms-neo4j-label-propagation
•Twitter Polarity Classification: https://dl.acm.org/citation.cfm?id=2140465

45. Link Prediction
Can we infer which new interactions
are likely to occur in the future?
“We formalize this question as the link
prediction problem, and develop
approaches to link prediction based on
measures for analyzing the
“proximity” of nodes in a network.”
Jon Kleinberg and David Liben-Nowell A Goal, an Approach
&
an Algorithm Category

46. What can we use this approach for?
● future associations in a terrorist network
● co-authorships in a citation network
● associations between molecules in a biology network
● interest in an artist or artwork

47. Predicting a link means that we are predicting some future behaviour
or an unobserved fact.
For example, in a citation network, we’re actually predicting the action
of two people collaborating on a paper.
What's common across all these use cases?

48. Based on number of potential
triangles / closing triangles
Concept is that if 2 strangers have
a friend/colleague in common,
they are more likely to be
introduced
Common Neighbours

49. More Resources
https://neo4j.com/sandbox-v2/
https://neo4j.com/graph-algorithms-book
https://neo4j.com/graphacademy/online-training/

50. Thank You!
50

51. Source: “Communities, modules and large-scale structure in networks“ - Mark Newman
Source: “Hierarchical structure and the prediction of missing links in networks”;
”Structure and inference in annotated networks” - A. Clauset, C. Moore, and M.E.J. Newman.
Graph Algorithms
Extract Structure and Infer Behavior

52. Centralities
• PageRank
○ Which nodes have the most overall influence
• Closeness
○ Which nodes are able to reach entire group the fastest
• Betweenness
○ Which nodes are the bridges between different clusters
(most shortest paths)
• Degree
○ The number of connections in/out of a node

53. Centralities
• PageRank
○ Which nodes have the most overall influence
• Closeness
○ Which nodes are able to reach entire group the fastest
• Betweenness
○ Which nodes are the bridges between different clusters
(most shortest paths)
• Degree
○ The number of connections in/out of a node

54. Source: Maven 7
Centralities
• PageRank
○ Which nodes have the most overall influence
• Closeness
○ Which nodes are able to reach entire group the fastest
• Betweenness
○ Which nodes are the bridges between different clusters
(most shortest paths)
• Degree
○ The number of connections in/out of a node