1. The document discusses Neo4j, the world's most popular graph database. It highlights Neo4j's customers in top retail, financial, and software firms and its presence in Silicon Valley and global offices. 2. Neo4j is used both on-premises and in the cloud as a database-as-a-service. The document also discusses Neo4j's graph data science capabilities and its rise in popularity from 2010 to 2020. 3. Going forward, Neo4j is focusing on cloud services and positioning developers at the center of its strategy and products like Neo4j Aura and the Graph Data Science Library.

1. 01/12/2020

3. ML/AI & Graphs Synergy
#1 Graph Platform Neo4j Customer Use Cases Unite Neo4j
Neo4j SuperiorityGraph DB Popularity

5. 7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
Creator of the Property
Graph and Cypher language
at the core of the GQL ISO
project
Thousands of Customers
World-Wide
HQ in Silicon Valley, offices
include London, Munich,
Paris & Malmo
Industry Leaders use Neo4j
On-Prem
DB-as-a-Service
In the Cloud

8. +
+
+
The world’s most popular
graph database, as a cloud
service

9. Neo4j Graph Data
Science Library
Neo4j
Database
Neo4j
Bloom
™

12. 2020-2030

13. Neo4j Aura Professional
+
+
+
+
+
The Shift
To The
Cloud

14. +
+
+
+
+
The Shift
To The
Cloud
Neo4j Aura Enterprise (Coming Soon)

15. Developers
at the
Center of
all we do
Developers at the center

16. Rise
of(Graph)
Data
Science
- Get more insight out
of the data you
already have.
- Add relationships
into your existing
workflows.
- Make better, more
accurate predictions.
Why Do Graph Data Science?

17. Neo4j Graph Data
Science Library
Scalable Graph
Algorithms & Analytics
Workspace
Native Graph
Creation & Persistence
Neo4j
Database
Visual Graph
Exploration
& Prototyping
Neo4j
Bloom
Practical Integrated Intuitive
Rise
of(Graph)
Data
Science
Neo4j for Graph Data Science

18. 2014 2015 2016 2017 2018 2019 20202013
The Rise of the Graph Database
2010 - 2020: From Model to Category
Full
Potential of
the Property
Graph Model

19. Oct
2020
1.
2.
3.
Rank
4.
5.
6.
7.
8.
9.
10.
Neo4j
Microsoft Azure Cosmos DB
ArangoDB
OrientDB
Virtuoso
Amazon Neptune
JanusGraph
GraphDB
FaunaDB
Dgraph
DBMS Database Model
Graph
Multi-model
Multi-model
Multi-model
Multi-model
Multi-model
Graph
Multi-model
Multi-model
Graph
Score
Oct
2020
Sep
2019
51.33
32.01
5.55
5.47
2.57
2.48
2.40
2.09
1.79
1.68
+0.71
+0.34
-0.25
-0.01
+0.01
+0.13
+0.06
+0.67
+0.07
+0.06
Full
Potential of
the Property
Graph Model
The Rise of the Graph Database
2010 - 2020: From Model to Category

20. Highest possible scores in:
● Performance
● Scalability
● Workloads
● Data management
● Data loading/ingestion
● Queries/search
● Use cases
● API/extensibility
● Transactions
● High availability and
disaster recovery
● Deployment options
Full
Potential of
the Property
Graph Model
The Rise of the Graph Database
2010 - 2020: From Model to Category

21. Full
Potential of
the Property
Graph Model
The Rise of the Graph Database
2010 - 2020: Sweet Spot Use Cases

23. •
•
•
•
•
•
•

24. Neo4j’s Secret Sauce!

26. Neo4j Graph Data
Science
Neo4j
Database
Neo4j Bloom

29. Graph Data Science is a science-driven
approach to gain knowledge from the
relationships and structures in data,
typically to power predictions.
Rise
of(Graph)
Data
Science
What is Graph Data Science?

30. Rise
of(Graph)
Data
Science
o Get more insight out of the
data you already have.
o Add relationships into your
existing workflows.
o Make better, more accurate
predictions.
Why Do Graph Data Science?

31. Neo4j Graph Data
Science Library
Scalable Graph
Algorithms & Analytics
Workspace
Native Graph
Creation & Persistence
Neo4j
Database
Visual Graph
Exploration
& Prototyping
Neo4j
Bloom
Practical Integrated Intuitive
Rise
of(Graph)
Data
Science
Neo4j for Graph Data Science

32. Pathfinding
& Search
Similarity
Community
Detection
Centrality &
Importance
Graph
Embeddings
Link
Prediction
50+ Graph Algorithms & ML techniques
Flexible Analytics Workspace
Rise
of(Graph)
Data
Science
The Graph Data Science Library

33. • Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• 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
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Euclidean Distance
• Cosine Similarity
• Node Similarity (Jaccard)
• Overlap Similarity
• Pearson Similarity
• Approximate KNN
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
... Auxiliary Functions:
• Random
graph generation
• Graph export
• One hot encoding
• Distributions & metrics
• Node2Vec
• Random Projections
• GraphSAGE
Rise
of(Graph)
Data
Science
50+ Graph Algorithms in Neo4j

34. Write Your Own
Algorithms
Machine Learning
Algorithms
Use matrix math and neural networks
to learn the structure of your graph
Train, store, and fit predictive
models within Neo4j
Leverage your embeddings to build
nearest neighbors graphs
Use our Pregel API to build your own
algorithms using our infrastructure
Graph Embeddings & Complex
Data Types
Model
Catalog
GDS 1.0
First enterprise
library for graph
data science.
Neo4j 4.0
Compatibility
GDS 1.2
GDS 1.1
Graph Mutability
& Expressivity
GDS 1.3
New Algorithms &
RBAC
Graph Native
Learning
GDS 1.4
Rise
of(Graph)
Data
Science
Introducing GDS 1.4

35. Graph algorithms to uncover
trends and patterns
Patterns
Pointers
Queries to answer questions
with connected data
Predictions
Graph-native ML learns the
topology of your graph to
uncover new facts
Rise
of(Graph)
Data
Science
Putting it all Together

36. NODES talks:
- Graph native learning: introducing GraphSAGE & the model catalog
- Write your own algorithms with the Pregel API
- Getting graph questions answered through Neo4j Bloom
Developers:
- Developer Guides: neo4j.com/developer/graph-data-science/
- GDS Sandbox: sandbox.neo4j.com/?usecase=graph-data-science
- GitHub: github.com/neo4j/graph-data-science
Books:
- Graph Algorithms: Practical Examples in Apache Spark & Neo4j:
neo4j.com/graph-algorithms-book/
- GDS For Dummies: neo4j.com/graph-data-science-for-dummies
Rise
of(Graph)
Data
Science
Neo4j Graph Data Science - Resources

38. 38

39. Community
Vs
Enterprise
What is the difference Between
Community vs Enterprise?
Neo4j Enterprise Edition Neo4j Community Edition

40. Neo4j
Enterprise
Architecture
What is the Neo4j Enterprise Architecture
Neo4j Graph
Data Science
Neo4j
Databas
e
Neo4j
Bloom

41. Neo4j
Runes
Everywhere
Where can I deploy Neo4j?
On-Prem DaaS – Fully ManagedIn the Cloud (BYOL)

42. 42