Graph Databases: insight, scandal and the speed you always wanted!
Details Data Engineers and Scientists are always intrigued by new data sources. From spreadsheets and SQL to No-SQL sources such as MongoDB and XML. In March we will hear from Neo4j, provider of a native graph (think network) database. As any practitioner of AI and Machine Learning knows, relationships provide critical insights to our understanding and forecasting capabilities. Learn how you can leverage these insights to work for you. ABSTRACT With the assistance of Neo4j's graph experts, the International Consortium for Investigative Journalism won the Pulitzer Prize for exposing the complex relationships hidden away in the Panama Papers of the wealthy, their money, government officials, and tax havens. Neo also provided them with expertise for analyzing the subsequent Paradise Papers. More recently, Neo helped MSNBC use graph techniques to successfully identify the Russian Trolls responsible for over 200k fake tweets during the 2016 election. Discover the techniques used in these projects and how they are being used successfully by many global organizations for their cybersecurity, identity resolution, and fraud use cases. BIOs Mark Quinsland is a pre-sales engineer based on the West Coast. He was a full-stack developer and consultant for the global automotive industry before becoming a pre/post sales engineer with a Santa Clara-based NoSQL & Graph startup. His true passion is helping people harness the power of tech to solve interesting problems. When not working or traveling, Mark can usually be found hiking with his dogs along nearby Folsom Lake. Justin Fine is based in Los Angeles, CA and is a Sales Engineer working mainly in the SoCal region. His academic background is applied mathematics and has worked with graphs for over 12 years in many different verticals while consulting (federal, telecoms, financial, etc). During this time as a consultant his focus was mainly advanced analytics utilizing NoSQL technologies. He recently comes from Microsoft's Azure team where he was a Data Solution architect and is very excited to be part of the Neo4j family! When Justin isn't nerding he enjoys scotch, cigars, and reading with his cat Penny.
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Graph Databases: insight, scandal and the speed you always wanted!
- 1. Offshore Leaks: Graph Databases For Investigative Journalism Justin Fine Santa Monica, CA Feb 2019 Panama Papers Paradise Papers
- 2. 2 GRAPHTOUR – San Francisco https://neo4j.com/graphtour/ https://www.eventbrite.com/e/graphtour-san-francisco-ca-tickets-58600670182
- 4. Machine Learning and Graph Algorithms in Neo4j • Parallel Breadth First Search & DFS • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • Minimum Spanning Tree • A* Shortest Path • Yen’s K Shortest Path • K-Spanning Tree (MST) • Degree Centrality • Closeness Centrality • CC Variations: Harmonic, Dangalchev, & Wasserman & Faust • Betweenness Centrality • Approximate Betweenness Centrality • PageRank • Personalized PageRank • ArticleRank • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity – 1 Step & Multi-Step • Balanced Triad (identification) • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Overlap Similarity • Random Walk • One Hot Encoding Reference Implementations for Graph Embeddings (Node to Vector) • DeepGL • DeepWalk Pathfinding & Search Centrality / Importance Community Detection Similarity & ML Workflow https://neo4j.com/docs/graph-algorithms/3.5/
- 5. Justin Fine Field Engineer , Neo4j Justin.Fine@neo4j.com https://www.linkedin.com/in/justin-fine- a7604523 https://github.com/JustinFineNeo/ IntroToNeo4J
- 19. What’s a Graph Database???
- 20. Chart Graph
- 22. Common Use Case ● Real time recommendations ● Fraud Detection ● Network & IT Management ● Social Networks ● Bill of Materials ● Knowledge Graphs ● Master Data Management ● Access Management ● Microservices Analysis ● IoT ● ...
- 23. Fixed Sized Records “Joins” on Creation Spin Spin Spin through this data structure Pointers instead of Lookups1 2 3 4 Neo4j Secret Sauce
- 24. Neo4j Mission Statement To help the world make sense of data
- 28. SQLER Diagrams
- 29. Relational Versus Graph Models Relational Model Graph Model KNOWS KNOWS KNOWS ANDREAS TOBIAS MICA DELIA Person FriendPerson- Friend ANDREAS DELIA TOBIAS MICA
- 30. Property Graph Model Components Nodes • The objects in the graph • Can have name-value properties • Can be labeled Relationships • Relate nodes by type and direction • Can have name-value properties CAR DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” LOVES LOVES LIVES WITH OW NS PERSON PERSON
- 31. Cypher: Powerful and Expressive Query Language CREATE (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} ) LOVES Dan Ann LABEL PROPERTY NODE NODE LABEL PROPERTY
- 32. Cypher Query Language SQL for graphs
- 34. Almost 200 journalists Based in 65 countries “Our aim is to bring journalists from different countries together in teams - eliminating rivalry and promoting collaboration. Together, we aim to be the world’s best cross-border investigative team.” icij.org/about
- 35. Exposed the offshore holdings of 12 current and former world leaders. And dealings of 128 more politicians and public officials around the world. Exposure of hidden Secrets
- 37. INSIDE THE 2.6 TB
- 38. Context is King name: “John” last: „Miller“ role: „Negotiator“ name: "Maria" last: "Osara" name: “Some Media Ltd” value: “$70M” PERSON PERSON $ @ name: ”Jose" last: “Pereia“ position: “Governor“ name: “Alice” last: „Smith“ role: „Advisor“ PERSON PERSON
- 39. Context is King name: “John” last: „Miller“ role: „Negotiator“ name: "Maria" last: "Osara" name: “Some Media Ltd” value: “$70M” PERSON PERSON $ @ name: ”Jose" last: “Pereia“ position: “Governor“ name: “Alice” last: „Smith“ role: „Advisor“ PERSON MENTIONS SUPPORTS PERSONSENT WROTE CONTAINS OW NS since: Jan 10, 2011 CREATED
- 40. We need a data model Meta Data Entities • Document, Email, Contract, DB- Record • Meta: Author, Date, Source, Keywords • Conversation: Sender, Receiver, Topic • Money Flows Actual Entities • Person • Representative (Officer) • Address • Client • Company • Account Either based on our use cases & questions On the entities present in our meta-data and data.
- 41. Data model – relationships Meta-Data • sent, received, cc‘ed • mentioned, topic-of • created, signed • attached • roles • family relationships Activities • open account • manage • has shares • registered address • money flow
- 42. The Basic ICIJ Data Model
- 43. The Real ICIJ Data Model
- 44. The Predictions
- 45. Actually found because of Neo4j
- 47. Bit more connected though
- 48. Make Sense Of Data https://neo4j.com/ sandbox-v2/
- 49. Neo4j Desktop For ICIJ https://offshoreleaks.icij.org/pages/database