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Neo4j Graph Data Science - Webinar

Marco Bessi, Neo4j
Riccardo Ciarlo, Neo4j

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Neo4j Graph Data Science - Webinar

  1. 1. Neo4j Graph Data Science ('Riccardo Ciarlo', '') -[:IS_MEMBER_OF]-> ('Country Manager', 'Neo4j', 'Italy') ('Marco Bessi', '') -[:IS_MEMBER_OF]-> ('Field Engineer PreSale', 'Neo4j', 'Italy')
  2. 2. 2 We help the world make sense of data The leader in Graph Databases 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 7/10 20/25 7/10 Top Retail Firms Top Financial Firms Top Software Vendors Industry Leaders use Neo4j
  3. 3. 3 Harnessing connections drives business value Enhanced Decision Making Hyper Personalization Massive Data Integration Data Driven Discovery & Innovation Product Recommendations Personalized Health Care Media and Advertising Fraud Prevention Network Analysis Law Enforcement Drug Discovery Intelligence and Crime Detection Product & Process Innovation 360 view of customer Compliance Optimize Operations Data Science AI & ML Fraud Prediction Patient Journey Customer Disambiguation Transforming Industries
  4. 4. Networks of People Transaction Networks Bought B ou gh t V i e w e d R e t u r n e d Bought Knowledge Networks Pl ay s Lives_in In_sport Likes F a n _ o f Plays_for E.g., Risk management, Supply chain, Payments E.g., Employees, Customers, Suppliers, Partners, Influencers E.g., Enterprise content, Domain specific content, eCommerce content K n o w s Knows Knows K n o w s Connections in data are as valuable as the data itself
  5. 5. Predictive Maintenance Churn Prediction Fraud Detection Life Sciences Personalized Recommendations Cybersecurity Disambiguation & Segmentation Search & Master Data Mgmt. Graph Data Science applications Just a few examples…
  6. 6. What is: Data science Graph data science Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions. Data scientists use data to answer questions. Data scientists use relationships to answer questions.
  7. 7. 7 Data science: it’s complicated Dozens of libraries, hundreds of algos & no docs! How do we shape data into a graph in the first place? We’ve picked a library...good luck learning the syntax What? We have to build the entire ETL pipeline for this? Are the results right? How do we get into production? Data Modeling Which Algorithms? Learn Syntax Reshape Data What Now?
  8. 8. 8 Simplify your experience! Dozens of libraries, hundreds of algos & no docs! We’ve picked a library...good luck learning the syntax What? We have to build the entire ETL pipeline for this? Are the results right? How do we get into production? Data Modeling Which Algorithms? Learn Syntax Reshape Data What Now? We have validated algos, clear docs, & tutorials Neo4j syntax is standardized and simplified Seamlessly reshape data with 1 command Simply write results to Neo4j & move to production With Neo4j it’s already a graph
  9. 9. Evolution of Graph Data Science
  10. 10. Evolution of Graph Data Science Decision Support Graph Based Predictions Graph Native Learning 10 Graph Feature Engineering Graph Embeddings Graph Networks Knowledge Graphs Graph Analytics
  11. 11. Evolution of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Networks 11 Graph Analytics Knowledge Graphs Graph search and queries Support domain experts Fast, local decisioning and pattern matching You know what you are looking for and making a decision
  12. 12. Evolution of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Networks 12 Knowledge Graphs Graph Analytics Graph queries & algorithms for offline analysis Understanding Structures Global analysis and iterations You are learning the overall structure of a network, updating data and predicting
  13. 13. Evolution of Graph Data Science Graph Embeddings Graph Networks 13 Knowledge Graphs Graph Analytics Graph Feature Engineering Graph algorithms & queries for machine learning Improve Prediction Accuracy Take advantage of hardened, validated graph algorithms that enable reasoning about network structure.
  14. 14. Evolution of Graph Data Science 14 Graph Feature Engineering Graph Embeddings Graph Networks Knowledge Graphs Graph Analytics Graph embeddings for dimensionality reduction Predictions on complex structures Embedding transforms graphs into a feature vector, or set of vectors, describing topology, connectivity, or attributes of nodes & relationships in the graph
  15. 15. Neoj4 for Graph Data Science
  16. 16. 16 for Graph Data Science (GDS) 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
  17. 17. 17 Neo4j Database: native graph technology Enterprise-grade native graph database and tooling: ▪ Store, reveal and query data relationships ▪ Traverse and analyze any levels of depth in real-time ▪ Add context to AI systems and network structures to data science • Performance • ACID Transactions • Schema-free Agility • Graph Algorithms Designed, built and tested natively for graphs from the start for: • Developer Productivity • Hardware Efficiency • Enterprise Scale • Index-free adjacency Analytics Tooling Graph Transactions Data Integration Dev. & Admin Drivers & APIs Discovery & Visualization Graph Analytics
  18. 18. 18 Neo4j GDS Library Robust Graph Algorithms ▪ Compute connectivity metrics and learn the topology of your graph ▪ Highly parallelized and scale to 10’s of billions of nodes Efficient & Flexible Analytics Workspace ▪ Automatically reshapes transactional graphs into an in-memory analytics graph ▪ Optimized for analytics with global traversals and aggregation ▪ Create workflows and layer algorithms Mutable In-Memory Workspace Computational Graph Native Graph Store
  19. 19. Neo4j GDS Library: Graph algorithms categories 19 Pathfinding and Search Centrality Community Detection Heuristic Link Prediction Similarity Determines the importance of distinct nodes in the network. Detects group clustering or partition. Evaluates how alike nodes are by neighbors and relationships. Finds optimal paths or evaluates route availability and quality. Estimates the likelihood of nodes forming a future relationship. 50+ graph algorithms in Neo4j Embeddings Learns graph topology to reduce dimensionality for ML
  20. 20. Neo4j Bloom: Built-in data visualization ▪ Explore graphs visually ▪ Prototype faster ▪ Visualize and discover ▪ Easy for non-technical users
  21. 21. 21 Neo4j Graph Data Science From Analytics to Graph-Native Machine Learning Graph algorithms to uncover trends and patterns Patterns Pointers Queries to answer questions with connected data Predictions Graph-native ML to use the topology of your graph to uncover new facts
  22. 22. GDS 1.5 What’s new
  23. 23. Memory Compression 75% smaller memory footprint using an enterprise graph projection Footprint Production Algorithms Performance improvements & new standard API for pathfinding Pathfinding Machine Learning Persistent & publishable models, node classification & link prediction Workflows Customizable Algorithms Pregel enhancements & algorithms (e.g. HITS & SLLPA) Pregel API Enhancements in GDS v1.5
  24. 24. Graph-Native Feature Engineering Train Predictive Model Queries Algorithms Embeddings 1. Model Type 2. Property Selection 3. Train & Test 4. Model Selection Supervised ML workflow in Neo4j Apply Model to Existing / New Data Use Predictions for Decisions Use Predictions to Enhance the Graph Publish & Share Store Model in Database
  25. 25. Use cases 25
  26. 26. 26 Top Graph Data Science Applications Fraud Marketing Customer Journey in Financial Services and Banking • First party & synthetic identity fraud • Fraud rings • Money laundering • Disambiguation • Recommendations • Customer segmentation • Churn prediction
  27. 27. 27 Top Graph Data Science Applications Market-To Supply Chain Logistics in Marketing and Supply Chain • Disambiguation • Recommendation • Customer segmentation • Logistics and routing • Predictive fulfillment • Risk identification • Supply chain driven product design
  28. 28. Media conglomerate with $3.2 Billion revenue Parent of: People, Travel+Leisure, Better Homes & Gardens... 28 Illuminating the Anonymous Neo4j GDS for Identify Disambiguation • Connect various data streams with 4.4 TB of data (14Bn nodes) • Graph algorithms to find unique users by behavior • 163Mn unique profile with richer & longer lived data • 612% Increase in visits per profile Challenge: Marketing in the Dark • Anonymous across sites & devices with aging cookies • External data is expensive and difficult to validate
  29. 29. 29 Top Graph Data Science Applications Discovery Patient Care Regulatory Compliance in Healthcare and Life Sciences • Drug repurposing • Knowledge graph completion • Risk identification & spread • Patient journey • Personalized care • Contact tracing
  30. 30. Medical device manufacturer with 10.74B annual revenue Manufacture products like pacemakers, stents and heart valves, all the way through diagnostic tests. Integrated development, design, manufacture, and sales. 30 Improving Reliability Neo4j GDS for supply chain & issues prediction Simple data model: parts, finished product, and failures • Knowledge Graph to support robust queries • Centrality algorithms to rank nodes based on their proximity to failures, similarity to find vulnerable components • Creating new data from connections in Neo4j Challenge: Predicting and preventing failures • Integrated supply chain: from raw materials to complex devices • Inconsistent analysis, unable to pinpoint cause of failures
  31. 31. Global pharmaceutical with $22.1Billion revenue Focus on oncology, cardiovascular, renal, metabolism, & respiratory 31 Improving Patient Outcomes Neo4j GDS to Map & Predict Patient Journeys • 3 yrs of visits, tests & diagnosis with 10’s of Bn of records • Knowledge Graph, graph queries & algorithms • Community detection to help find similarities over time • Finding earlier influence points to guide and assist Challenge: Better intervention for complex diseases • Complex diseases develop over years with many touch points • How can we intervene faster & improve outcomes?
  32. 32. Q&A ('Riccardo Ciarlo', '') -[:IS_MEMBER_OF]-> ('Country Manager', 'Neo4j', 'Italy') ('Marco Bessi', '') -[:IS_MEMBER_OF]-> ('Field Engineer PreSale', 'Neo4j', 'Italy')