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![Who am I … Andrew Frei
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(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](https://image.slidesharecdn.com/introneo4jde-201016133411/75/Einfuhrung-in-Neo4j-3-2048.jpg)
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Einführung in Neo4j
The document introduces Neo4j, a graph database, and discusses its applications like social networks and fraud detection. It explains the labeled property graph model and shows examples of how it can represent relationships between people, locations, and objects. The document also lists several industries and use cases where Neo4j has helped companies connect disparate data and enable new insights through its graph capabilities.
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Einführung in Neo4j
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- 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 weuse to work; look at it…..
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- 6. 6 Look at itagain, now as a graph
- 7. 7 The Graph ProblemProblem Many organizations don’t realize that they have a graph problem.
- 8. Labeled Property GraphModel 88 • Nodes – Represent objects in the graph
- 9. DRIVES Labeled Property GraphModel 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: May29, 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: May29, 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 ModelIs the Physical Model
- 13. 13 Industries - UseCases 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
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- 16. Adobe Behance Social Networkof 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”
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- 18. Dun & Bradstreet Neo4jfor 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 FraudDetection / 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 DataScience™ 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 Hanksis 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?
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- 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 TopFinancial 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
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- 29. Contact details 29 Andrew Frei SalesManager 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