Neo4j GraphTalk Helsinki - Introduction and Graph Use Cases
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This document provides an introduction to graphs and Neo4j. It discusses that Neo4j is a native graph database that allows organizations to leverage connections in data in real-time to create value. It then provides information on Neo4j as a company and as a product, including that it is the world's leading graph database. The document goes on to define what graphs are from a data structure perspective and provides examples of famous graphs like social networks. It discusses why graph databases are useful compared to relational databases for representing complex, connected data and provides examples of use cases for Neo4j like recommendations, fraud detection, and network analysis.
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Neo4j GraphTalk Helsinki - Introduction and Graph Use Cases
- 1. Intro to Graphs and Neo4j
- 2. Neo4j Inc. is the Creator of a highly scalable, native graph database. Neo4j gives any organization the ability to leverage connections in data — in real-time to create value
- 3. Neo4j - The Company ● Creator of Neo4j ● ~250 employees with HQ in Silicon Valley, London, Munich, Paris, Stockholm and Malmö (R&D) Neo4j - The Product ● Neo4j - World’s leading graph database ● 2M+ downloads, adding 50k+ per month ● ~200 enterprise subscription customers including over 50 of the Global 2000
- 5. First, Graphs are not Charts
- 8. Knows Know s Know s Know s Social Graph “People you may know” Disruptor: Facebook Industry: Media Ad-business Bough t Bough t Viewe d Returned Bough t Disruptor: Amazon Industry: Retail People & Products“Other people also bought” Whatche d W atche d W atche d Like s Like d Rate d People & Content“You might also like” Disruptor: Netflix Industry: Broadcasting Media Some Famous Graphs
- 10. Biggest Leak in History Paradise Papers Offshore Leaks Database Snowden – NSA WikiLeaks Cablegate Pentagon Papers 0 5M 10M Documents Leaked
- 15. Why Graphs
- 16. Connected Data What Can We Learn From Google? Isolated Data
- 18. Hierarchies On Stage Business Processes Behind the Scene Data Structure Linear Supply Chain Information
- 19. On Stage Behind the Scene Data Structure Business Processes Organizations Multi-related Knowledge
- 20. On Stage Behind the Scene Business Processes Data Structure Organizations Multi-related Knowledge
- 21. So that was Graphs Now let’s talk about Graph Databases
- 22. Why Graph Databases RELATIONAL DATABASES ARE GREAT
- 23. Why Graph Databases But …. Again and again the same problem appears • Volume • Complexity
- 24. Why Graph Databases Task oriented databases The right tool for the job
- 26. DATA DATA A way of representing data
- 27. Relational Database Structured Pre-computed Based on rigid rules A way of representing data
- 28. Graph Database Relational Database A way of representing data Highly Flexible Real-Time Queries Highly Contextual Structured Pre-computed Based on rigid rules
- 29. Relational is simple...until it gets complicated... Relational to Graph 29 table1 table2join-table
- 30. You know relational...now consider relationships... Relational to Graph 30 actors moviesactor_movie
- 31. Index free adjacency: Unlike other database models Neo4j connects data as it stores it Index-free adjacency ensures lightning-fast retrieval of data and relationships Neo4j Advantage - Performance
- 32. How Neo4j Differentiates from other Databases Visualization Queries Processing Storage Non-Native Graph DB SQLCypher (graphs)-[are]->(everywher e) Cypher/Gremlin /Proprietary Tabl e Tabl e Native Graph DB RDBMS Tabl e Key-Valu e Column Optimized for graph workloads
- 33. Looks different. Who cares? • a sample social graph with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O • Graph Locality # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms
- 34. Connectedness and Size of Data Set ResponseTime Relational and Other NoSQL Databases 0 to 2 hops 0 to 3 degrees Thousands of connections 1000x Advantage Tens to hundreds of hops Thousands of degrees Billions of connections Graph “Minutes to milliseconds” “Minutes to Milliseconds” Real-Time Query Performance
- 35. Why Graph Databases? Performance - for specific workloads Index-free adjacency: everything KNOWS its neighbours Graph-locality: unnecessary scope is quickly moved out of sight “Minutes to milliseconds performance”
- 36. 36 Graph Platform Development & Administration Analytics Tooling BUSINESS USERS DEVELOPERS ADMINS Graph Analytics Graph Transactions Data Integration Discovery & Visualization DATA ANALYSTS DATA SCIENTISTS Drivers & APIs APPLICATIONS AI BIG DATA IT
- 37. What are people using graph databases for?
- 38. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 39. VIEWED GRAPH THINKING: Real Time Recommendations VIEWED BOUGHT VIEWED BOUGHT BOUGHT BOUGHT BOUGHT NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 40. “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.” Marcos Wada Software Developer, Walmart NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 41. GRAPH THINKING: Master Data Management MANAGE S MANAGE S LEADS REGION M ANAG ES MANAGE S REGION LEADS LEADS COLLABORATES NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 42. Neo4j is the heart of Cisco HMP: used for governance and single source of truth and a one-stop shop for all of Cisco’s hierarchies. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 43. O PEN ED _AC C O U N THAS IS_ISSUED GRAPH THINKING: Fraud Detection HAS LIVES LIVES IS_ISSUED OPENED_ACCOUNT NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 44. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis
- 45. Revolving Debt Number of Accounts Normal behavior Fraudulent pattern Fraud Detection with Connected Analysis
- 46. CONNECTED ANALYSIS Endpoint-Centric Analysis of users and their end-points Navigation Centric Analysis of navigation behavior and suspect patterns Account-Centric Analysis of anomaly behavior by channel DISCRETE ANALYSIS 1 . 2 . 3 . Cross Chanel Analysis of anomaly behavior correlated across channels 4 . Entity Linking Analysis of relationships to detect organized crime and collusion 5 . Augmented Fraud Detection
- 47. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
- 48. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURE D LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph
- 49. ACCOUNT HOLDER 2 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph ACCOUNT HOLDER 1
- 50. “Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.” Gorka Sadowski Cyber Security Expert NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 51. BROWSES CONNECTS BRIDGES ROUTES POWERS ROUTES POWERS POWERS HOSTS QUERIES GRAPH THINKING: Network & IT-Operations NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 52. Uses Neo4j for network topology analysis for big telco service providers NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 53. GRAPH THINKING: Identity And Access Management TRUSTS TRUSTS ID ID AUTHENTICATES AUTHENTICATES O W NS OWNS CAN_READ NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 54. UBS was the recipient of the 2014 Graphie Award for “Best Identity And Access Management App” NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 55. Graph Querying
- 56. A pattern matching query language made for graphs 56  Cypher the SQL of Graphs ● Declarative ○ You tell Cypher what you want, not how to do it ● Expressive ○ Syntax optimized for reading by humans ● Pattern Matching ○ Patterns are easy for your human brain
- 57. Pattern in our Graph Model LOVES Dan Ann NODE NODE Relationship
- 58. Pattern in our Graph Model LOVES Dan Ann NODE NODE Relationship () --> ()
- 59. Cypher: Express Graph Patterns (:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} ) LOVES Dan Ann LABEL PROPERTY NODE NODE LABEL PROPERTY Relationship TYPE
- 60. Cypher: CREATE Graph Patterns CREATE (:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} ) LOVES Dan Ann LABEL PROPERTY NODE NODE LABEL PROPERTY Relationship TYPE
- 61. Cypher: MATCH Graph Patterns MATCH (:Person { name:"Dan"} ) -[:LOVES]-> ( whom ) RETURN whom LOVES Dan ? VARIABLE NODE NODE LABEL PROPERTY Relationship TYPE
- 62. Demo! Data model
- 65. Demo! How to get Started
- 66. How to get Started / Movie Graph
- 68. eBay Shopbot It’s all about context
- 69. Relational Database Customer Cust_Order Order Customer Order Address Cust_Addr Address Addr_City City Warehouse Product ProductOrd_Prod City Prod_Wrh WarehouseCity_Wrh Graph Database Relational Database
- 72. Relational Database Customer Cust_Order Order Customer Order Address Cust_Addr Address Graph Database Addr_City City Warehouse Product ProductOrd_Prod City
- 73. • Relationships are first class citizen • No need for joins, just follow pre-materialized relationships of nodes • Query & Data-locality – navigate out from your starting points • Only load what’s needed • Aggregate and project results as you go • Optimized disk and memory model for graphs High Query Performance with a Native Graph DB
- 74. Index free adjacency: Unlike other database models Neo4j connects data as it stores it Index-free adjacency ensures lightning-fast retrieval of data and relationships Neo4j Advantage - Performance
- 75. name: Tom Hanks born: 1956 title: Cloud Atlas released: 2012 title: The Matrix released: 1999 name: Lana Wachowski born: 1965 ACTED_IN roles: Zachry ACTED_IN roles: Bill Smoke DIRECTED DIRECTED ACTED_IN roles: Agent Smith name: Hugo Weaving born: 1960 Person Movie Movie Person Director ActorPerson Actor Whiteboard friendliness
- 76. Bank US Account Company Bank Bahamas Person B Person A Person A NODE RELATIONSHIP R EG ISTER ED IS_OFFICER_OF LIVES_AT LIVES_AT HAS WITH
- 78. This is data modelled as graph! Graph Database
- 79. Graph Database Relational Database A way of representing data Highly Flexible Real-Time Queries Highly Contextual Structured Pre-computed Based on rigid rules
- 80. If it’s not stored in tables how is it stored? • Data stored on disk is all linked lists of fixed size records. Properties are stored as a linked list of property records, each holding a key and value and pointing to the next property. Each node and relationship references its first property record. The Nodes also reference the first relationship in its relationship chain. Each Relationship references its start and end node. It also references the previous and next relationship record for the start and end node respectively. • Example of a node and its relationship(s) stored on disk:
- 81. cypher The SQL of Graphs
- 82. 82  • Declarative • Expressive • Pattern Matching Cypher
- 83. The components of a Cypher query MATCH path = (:Person)-[:ACTED_IN]->(:Movie) RETURN path MATCH and RETURN are Cypher keywords path is a variable :Movie is a node label :ACTED_IN is a relationship type
- 84. Super short Demo :Play movie-graph
- 85. Intro to the property graph model
- 86. Neo4j Fundamentals • Nodes • Relationships • Properties • Labels
- 87. Car Property Graph Model Components Nodes • Represent the objects in the graph • Can be labeled Person Person
- 88. Car DRIVES Property Graph Model Components Nodes • Represent the objects in the graph • Can be labeled Relationships • Relate nodes by type and direction LOVES LOVES LIVES WITH OW NS Person Person
- 89. Car DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Property Graph Model Components 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. LOVES LOVES LIVES WITH OW NS Person Person
- 90. Summary of the graph building blocks • Nodes - Entities and complex value types • Relationships - Connect entities and structure domain • Properties - Entity attributes, relationship qualities, metadata • Labels - Group nodes by role
- 91. Whiteboard Friendliness Easy to design and modeldirect representation of the model
- 93. Tom Hanks Hugo Weaving Cloud Atlas The Matrix Lana Wachowski ACTED_IN ACTED_IN ACTED_IN DIRECTED DIRECTED Whiteboard friendliness
- 94. name: Tom Hanks born: 1956 title: Cloud Atlas released: 2012 title: The Matrix released: 1999 name: Lana Wachowski born: 1965 ACTED_IN roles: Zachry ACTED_IN roles: Bill Smoke DIRECTED DIRECTED ACTED_IN roles: Agent Smith name: Hugo Weaving born: 1960 Person Movie Movie Person Director ActorPerson Actor Whiteboard friendliness
- 96. How do you use Neo4j - Often asked because Neo4j is more than a database
- 97. Neo4j Fits into Your Enterprise Environment Data Storage and Business Rules Execution Data Mining and Aggregation Application Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analytic Infrastructure Graph Compute Engine EDW … Data Scientist End User Databases Relational NoSQL Hadoop
- 98. In your persistence layer, switch to • Official Neo4j Drivers with Cypher • Community Drivers • Neo4j-JDBC Driver • Object-graph-mapping library • Neo4j-ogm • Spring Data Neo4j • Py2neo • neo4j-php-client (PHP) • Other libraries available for analytics pipeline, ETL and BI tools • Using Neo4j from your Application
- 99. 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
- 100. Why Graphs THE MODEL Gives you a hi-fi representation of reality Is so much easier to store highly connected domains Is very flexible in dynamic, agile environments