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Einführung in Neo4j
2
• Die primären Anwendungsfälle von Graphdatenbanken
• Die ‚Secret Sauce‘ von Neo4j, die diese möglich machen
• Die Visualisierung von Graphen
• Einfach mit Neo4j loslegen, aber wie?
Who am I … Andrew Frei
3
(Andrew) -[:LIVES_NEAR]->(Zurich)
(Andrew) -[:HAS_SHOESIZE]-> (47)
(Andrew) -[:IS_TODAYS]-> (host)
(Andrew) -[:LOVES]-> (Neo4j)
(Andrew) -[:LOVES_SELLING]-> (Neo4j)
Tagline: Supporting Companies to 'Connect the Dots' with Graph Databases & Analytics
Contact: linkedin.com/in/andrew-frei/ and andrew.frei@neo4j.com
4
The way we use to work; look at it…..
5
Swap Glasses…
6
Look at it again, now as a graph
7
The Graph Problem Problem
Many organizations don’t realize that they have a graph problem.
Labeled Property Graph Model
88
• Nodes – Represent objects
in the graph
DRIVES
Labeled Property Graph Model
MARRIED TO
LIVES WITH
OW
NS
99
• Nodes – Represent objects
in the graph
• Relationship – Relate
nodes by type and direction
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Latitude: 37.5629900°
Longitude: -122.3255300°
Labeled Property Graph Model
MARRIED TO
LIVES WITH
OW
NS
1010
• Nodes – Represent objects
in the graph
• Relationship – Relate
nodes by type and direction
• Property – Name-value
pairs that can go on nodes
and relationships
CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Latitude: 37.5629900°
Longitude: -122.3255300°
Labeled Property Graph Model
MARRIED TO
LIVES WITH
OW
NS
PERSON PERSON
1111
• Nodes – Represent objects
in the graph
• Relationship – Relate
nodes by type and direction
• Property – Name-value
pairs that can go on nodes
and relationships
• Label – Group nodes and
shape the domain
The Whiteboard Model Is the Physical Model
13
Industries - Use Cases
Telco & Utilities
Banks &
Insurers, Gov’t
Media, Software & IT
companies
Life Sciences &
Pharma
Manufacturing
& Logistics
eCommerce, other
industries
Connected Data - Use Cases
Network &
IT Operations
Fraud
Detection
Identity & Access
Management
Knowledge
Graph
Master Data
Management
Real-Time
Recommendations
15
Adobe Behance
Social Network of 10M
Graphic Artists
Background
● Social network of 10M graphic artists
● Peer-to-peer evaluation of art and works-in-progress
● Job sourcing site for creatives
● Massive, millions of updates (reads & writes) to Activity Feed
● 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
● Artists subscribe, appreciate and curate “galleries” of works of their own
and from other artists
● Activities Feed is how everyone receives updates
● 1st implementation was 150 MongoDB instances
● 2nd implementation shrunk to 48 Cassandras, but it was still too slow and
required heavy IT overhead
Solution and Benefits
● 3rd implementation shrunk to 3 Neo4j instances
● Saved over $500k in annual AWS fees
● Reduced data footprint from 50TB to 40GB
● Significantly easier to introduce new features like, “New projects in your
Network”
17
Dun & Bradstreet
Neo4j for Tracking Beneficial
Ownership
Background
● Regulations and requirements around beneficial
ownership
● Needed to let B2B clients book new business promptly
via accelerated due diligence investigations
Business Problem
● Investigations call for highly trained staff, and this activity is
hard to scale. A single request might tie up key people for
10-15 days, resulting in lost revenue
Solution and Benefits
● Use Neo4j to quickly query historic relationships between
business owners and companies
● Query responses take milliseconds versus days of skilled
manual research
ICIJ Panama Papers
Fraud Detection /
Graph-Based Search
Background
● International investigative team specializing in
cross-border crime, corruption and accountability of
power
Business Problem
● Find relationships between people, accounts, shell companies
and offshore accounts
● Biggest “Snowden-Style” document leak ever; 11.5 million
documents, 2.6TB of data
Solution and Benefits
● Pulitzer Prize winning investigation resulted in robust
coverage of fraud and corruption
● PM of Iceland resigned, exposed Putin, Prime Ministers,
gangsters, celebrities (Messi) - Trials are ongoing
How Neo4j Fits — Common Architecture Patterns
From Disparate Silos
To Cross-Silo Connections
From Tabular Data
To Connected Data
From Data Lake Analytics
to Real-Time Operations
for Graph Data Science™
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
• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• Balanced Triad (identification)
50+ Graph Algorithms in Neo4j
• 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
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
... Auxiliary Functions:
• Random
graph generation
• Graph export
• One hot encoding
• Distributions & metrics
Embeddings
• Node2Vec
• Random Projections
• GraphSAGE
Mission
23
❏ Tom Hanks is getting old(er)...
❏ We can boost his career and put him together with a few new faces. Who do
you recommend? How would you give weight to your recommendation?
24
Analytics
Tooling
Graph
Transactions
Dev.
& Admin
Graph Analytics &
Data Science
25
Applications Business Users
Native Graph Technology for Applications & Analytics
Data Analysts
Data Scientists
Drivers & APIs Discovery & Visualization
Data Integration
Developers
Admins
7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
Neo4j - The Graph Company
The Industry’s Largest Dedicated Investment in Graphs
26
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
27 neo4j.com/sandbox
28
Q & A
Contact details
29
Andrew Frei
Sales Manager Switzerland & Austria
Neo4j
linkedin.com/in/andrew-frei/
andrew.frei@neo4j.com
+41 78 793 42 56
www.neo4j.com
Bruno Ungermann
Sales Manager Germany
Neo4j
linkedin.com/in/brunoungermann/
bruno.ungermann@neo4j.com
www.neo4j.com

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Einführung in Neo4j

  • 1. 1
  • 2. Einführung in Neo4j 2 • Die primären Anwendungsfälle von Graphdatenbanken • Die ‚Secret Sauce‘ von Neo4j, die diese möglich machen • Die Visualisierung von Graphen • Einfach mit Neo4j loslegen, aber wie?
  • 3. Who am I … Andrew Frei 3 (Andrew) -[:LIVES_NEAR]->(Zurich) (Andrew) -[:HAS_SHOESIZE]-> (47) (Andrew) -[:IS_TODAYS]-> (host) (Andrew) -[:LOVES]-> (Neo4j) (Andrew) -[:LOVES_SELLING]-> (Neo4j) Tagline: Supporting Companies to 'Connect the Dots' with Graph Databases & Analytics Contact: linkedin.com/in/andrew-frei/ and andrew.frei@neo4j.com
  • 4. 4 The way we use to work; look at it…..
  • 6. 6 Look at it again, now as a graph
  • 7. 7 The Graph Problem Problem Many organizations don’t realize that they have a graph problem.
  • 8. Labeled Property Graph Model 88 • Nodes – Represent objects in the graph
  • 9. DRIVES Labeled Property Graph Model MARRIED TO LIVES WITH OW NS 99 • Nodes – Represent objects in the graph • Relationship – Relate nodes by type and direction
  • 10. DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Latitude: 37.5629900° Longitude: -122.3255300° Labeled Property Graph Model MARRIED TO LIVES WITH OW NS 1010 • Nodes – Represent objects in the graph • Relationship – Relate nodes by type and direction • Property – Name-value pairs that can go on nodes and relationships
  • 11. CAR DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Latitude: 37.5629900° Longitude: -122.3255300° Labeled Property Graph Model MARRIED TO LIVES WITH OW NS PERSON PERSON 1111 • Nodes – Represent objects in the graph • Relationship – Relate nodes by type and direction • Property – Name-value pairs that can go on nodes and relationships • Label – Group nodes and shape the domain
  • 12. The Whiteboard Model Is the Physical Model
  • 13. 13 Industries - Use Cases Telco & Utilities Banks & Insurers, Gov’t Media, Software & IT companies Life Sciences & Pharma Manufacturing & Logistics eCommerce, other industries
  • 14. Connected Data - Use Cases Network & IT Operations Fraud Detection Identity & Access Management Knowledge Graph Master Data Management Real-Time Recommendations
  • 15. 15
  • 16. Adobe Behance Social Network of 10M Graphic Artists Background ● Social network of 10M graphic artists ● Peer-to-peer evaluation of art and works-in-progress ● Job sourcing site for creatives ● Massive, millions of updates (reads & writes) to Activity Feed ● 150 Mongos to 48 Cassandras to 3 Neo4j’s! Business Problem ● Artists subscribe, appreciate and curate “galleries” of works of their own and from other artists ● Activities Feed is how everyone receives updates ● 1st implementation was 150 MongoDB instances ● 2nd implementation shrunk to 48 Cassandras, but it was still too slow and required heavy IT overhead Solution and Benefits ● 3rd implementation shrunk to 3 Neo4j instances ● Saved over $500k in annual AWS fees ● Reduced data footprint from 50TB to 40GB ● Significantly easier to introduce new features like, “New projects in your Network”
  • 17. 17
  • 18. Dun & Bradstreet Neo4j for Tracking Beneficial Ownership Background ● Regulations and requirements around beneficial ownership ● Needed to let B2B clients book new business promptly via accelerated due diligence investigations Business Problem ● Investigations call for highly trained staff, and this activity is hard to scale. A single request might tie up key people for 10-15 days, resulting in lost revenue Solution and Benefits ● Use Neo4j to quickly query historic relationships between business owners and companies ● Query responses take milliseconds versus days of skilled manual research
  • 19. ICIJ Panama Papers Fraud Detection / Graph-Based Search Background ● International investigative team specializing in cross-border crime, corruption and accountability of power Business Problem ● Find relationships between people, accounts, shell companies and offshore accounts ● Biggest “Snowden-Style” document leak ever; 11.5 million documents, 2.6TB of data Solution and Benefits ● Pulitzer Prize winning investigation resulted in robust coverage of fraud and corruption ● PM of Iceland resigned, exposed Putin, Prime Ministers, gangsters, celebrities (Messi) - Trials are ongoing
  • 20. How Neo4j Fits — Common Architecture Patterns From Disparate Silos To Cross-Silo Connections From Tabular Data To Connected Data From Data Lake Analytics to Real-Time Operations
  • 21. for Graph Data Science™ 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
  • 22. • Degree Centrality • Closeness Centrality • Harmonic Centrality • Betweenness Centrality & Approx. • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • Balanced Triad (identification) 50+ Graph Algorithms in Neo4j • 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 Pathfinding & Search Centrality / Importance Community Detection Similarity Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors ... Auxiliary Functions: • Random graph generation • Graph export • One hot encoding • Distributions & metrics Embeddings • Node2Vec • Random Projections • GraphSAGE
  • 23. Mission 23 ❏ Tom Hanks is getting old(er)... ❏ We can boost his career and put him together with a few new faces. Who do you recommend? How would you give weight to your recommendation?
  • 24. 24
  • 25. Analytics Tooling Graph Transactions Dev. & Admin Graph Analytics & Data Science 25 Applications Business Users Native Graph Technology for Applications & Analytics Data Analysts Data Scientists Drivers & APIs Discovery & Visualization Data Integration Developers Admins
  • 26. 7/10 20/25 7/10 Top Retail Firms Top Financial Firms Top Software Vendors Anyway You Like It Neo4j - The Graph Company The Industry’s Largest Dedicated Investment in Graphs 26 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
  • 29. Contact details 29 Andrew Frei Sales Manager Switzerland & Austria Neo4j linkedin.com/in/andrew-frei/ andrew.frei@neo4j.com +41 78 793 42 56 www.neo4j.com Bruno Ungermann Sales Manager Germany Neo4j linkedin.com/in/brunoungermann/ bruno.ungermann@neo4j.com www.neo4j.com