Graph Data Science: The Secret to Accelerating Innovation with AI/ML
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Speaker: Alicia Frame, Director – Graph Data Science, Neo4j
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Graph Data Science: The Secret to Accelerating Innovation with AI/ML
- 1. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 1 Graph Data Science: Accelerating AI & Machine Learning Alicia Frame, PhD Director, Data Science @ Neo4j
- 2. Neo4j, Inc. All rights reserved 2021 2 Neo4j: The Connected Data Company 20 of the top 25 financial firms 7 of the top 10 retailers 7 of the top 10 software vendors Neo4j is the creator of: • The world’s leading graph database • The first graph data science platform • The most flexible graph data model • The easiest-to-use graph query language Thousands of Organizations Use Neo4j Silicon Valley | London | Munich Paris | Malmö
- 3. Neo4j, Inc. All rights reserved 2021 3 Node Represents an entity in the graph Relationship Connect nodes to each other Property Describes a node or relationship: e.g. name, age, weight etc What’s a graph? MICA ANDRE Name: “Andre” Born: May 29, 1970 Twitter: “@dan” Name: “Mica” Born: Dec 5, 1975 CAR Brand “Volvo” Model: “V70” Since: Jan 10, 2011 LOVES LOVES LOVES LIVES WITH O W N S D R I V E S
- 4. Neo4j, Inc. All rights reserved 2021 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 Risk management, Supply chain, Orders, Payments, etc. Employees, Customers, Suppliers, Partners, Influencers, etc. Enterprise content, Domain specific content, eCommerce content, etc K n o w s Knows Knows K n o w s 4 Everything is Naturally Connected
- 5. Neo4j, Inc. All rights reserved 2021 5 Higher Pay and More Promotions • People Near Structural Holes • Organizational Misfits Network Structure is Highly Predictive Photo by Helena Lopes on Unsplash “Organizational Misfits and the Origins of Brokerage in Intrafirm Networks” A. Kleinbaum “Structural Holes and Good Ideas” R. Burt
- 6. Neo4j, Inc. All rights reserved 2021 Consider What Drives Your Business It’s not the numbers, it’s the relationships behind them Plants Warehouses Suppliers Distributors Competitors Partners Regulations Employees Citizens Customers Products Parts Services Regions
- 7. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 7 Relationships are the strongest predictors of behavior But You Can’t Analyse What You Can’t See ● Most data science techniques ignore relationships ● It’s painful to manually engineer connected features from tabular data ● Graphs are built on relationships, so… ● You don’t have to guess at the correlations: with graphs, relationships are built in James Fowler
- 8. Neo4j, Inc. All rights reserved 2021 8 8 Top 10 Tech Trends in Data and Analytics, 16 Feb 2021 According to Gartner, “Graphs form the foundation of modern D&A, with capabilities to enhance and improve user collaboration, ML models and explainable AI. The recent Gartner AI in Organizations Survey demonstrates that graph techniques are increasingly prevalent as AI maturity grows, going from 13% adoption when AI maturity is lowest to 48% when maturity is highest.” AI Research Papers Featuring Graph Source: Dimensions Knowledge System 4x Increase in traffic to Neo4j GDS page in 2H-2020 Analytics & Data Science Interest Exploding in Neo4j Community 100k+ Practicing data scientists engaged with Neo4j +210k downloads
- 9. Neo4j, Inc. All rights reserved 2021 9 Queries Find the patterns you know exist. Machine Learning Uncover trends and make predictions Visualization Explore, collaborate, and explain Graphs & Data Science Analytics Feature Engineering Data Exploration Graph Data Science Queries Machine Learning Visualization
- 10. Neo4j, Inc. All rights reserved 2021 10 Graphs & Data Science Knowledge Graphs Graph Algorithms Graph Native Machine Learning Find the patterns you’re looking for in connected data Use unsupervised machine learning techniques to identify associations, anomalies, and trends. Use embeddings to learn the features in your graph that you don’t even know are important yet. Train in-graph supervise ML models to predict links, labels, and missing data.
- 11. Neo4j, Inc. All rights reserved 2021 Better Predictions with Data You Already Have ● Traditional ML ignores network structure because it’s difficult to extract ● Uncover patterns and trends you can’t find any other way ● Easily generate predictive features to incorporate into ML pipelines 11 Machine Learning Pipeline
- 12. Neo4j, Inc. All rights reserved 2021 Neo4j’s Graph Data Science Framework Neo4j Graph Data Science Library Neo4j Database Neo4j Bloom Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Visual Graph Exploration & Prototyping
- 13. Neo4j, Inc. All rights reserved 2021 Robust Graph Algorithms & ML methods ● Compute metrics about the topology and connectivity ● Build predictive models to enhance your graph ● Highly parallelized and scale to 10’s of billions of nodes 13 The Neo4j GDS Library Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace ● Automatically reshapes transactional graphs into an in-memory analytics graph ● Optimized for global traversals and aggregation ● Create workflows and layer algorithms ● Store and manage predictive models in the model catalog
- 14. Neo4j, Inc. All rights reserved 2021 Community Detection 14 Neo4j’s Graph Data Science Library Unsupervised Graph Algorithms Clustering Dimension Reduction (generalization) Association Which parts of my graph are connected to each other? Which nodes are most similar? How important is each node? Supervised Machine Learning Node Classification Link Prediction Where will connections form next? What’s the label for this node? Centrality Embeddings Similarity Pathfinding More Algos than any other vendor ONLY in neo4j
- 15. Neo4j, Inc. All rights reserved 2021 15 Unsupervised Machine Learning Pathfinding & Search • 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 Centrality & Importance • Degree Centrality • Closeness Centrality • Harmonic Centrality • Betweenness Centrality & Approx. • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Hyperlink Induced Topic Search (HITS) • Influence Maximization (Greedy, CELF) Community Detection • Triangle Count • Local Clustering Coefficient • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • K-1 Coloring • Modularity Optimization • Speaker Listener Label Propagation Heuristic Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors Similarity • Node Similarity • K-Nearest Neighbors (KNN) • Jaccard Similarity • Cosine Similarity • Pearson Similarity • Euclidean Distance • Approximate Nearest Neighbors (ANN) Graph Embeddings • Node2Vec • FastRP • FastRPExtended • GraphSAGE
- 16. Neo4j, Inc. All rights reserved 2021 Graph Features & Graph Models for Predictions Traditional ML problems where relationships between your data points are important predictive features 16 Predictions influenced by graph structure Predictions about graph structure Enhance your graph by predicting missing data or changes to your graph that will occur in the future
- 17. Neo4j, Inc. All rights reserved 2021 17 Neo4j’s In-Graph ML Models Node classification: “What kind of node is this?” Link prediction: “Should there be a relationship between these nodes?” Labeled data: Pairs of nodes that are either linked or not Features: Pre-existing attributes, algorithms (pageRank), embedding
- 18. Neo4j, Inc. All rights reserved 2021 18 The Only Completely In-Graph, ML Workflow Graph-Native Feature Engineering Train Predictive Model Queries Algorithms Embeddings 1. Model Type 2. Property Selection 3. Train & Test 4. Model Selection Apply Model to Existing / New Data Use Predictions for Decisions Use Predictions to Enhance the Graph Publish & Share Store Model in Database
- 19. Neo4j, Inc. All rights reserved 2021 What’s most important and influential in my business? What’s occurring that’s unusual? What’s going to happen next? But traditional approaches to data make it impossible to reveal and effectively use those connections as data sizes become large Predictive signals get lost in big data noise 19 Graph Data Science Answers the BIG Questions Connected Data is Powerful Graph Data Science uses Connections to Answer Critical Questions
- 20. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 20 Resources Graph Resources ● Video: Advantages of Graph Technology ● Code: https://github.com/neo4j/graph-data-science/ ● Whitepaper: Financial Fraud Detection with Graph Data Science ● Case Study: Meredith Corporation Neo4j BookShelf ● Graph Databases For Dummies ● Graph Data Science For Dummies ● O’Reilly Graph Algorithms