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Graph Data Science with Neo4j: Nordics Webinar

Alicia Frame, Neo4j

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Graph Data Science with Neo4j: Nordics Webinar

  1. 1. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 1 Graph Data Science with Neo4j: Nordics Webinar Alicia Frame, PhD Director of Product Management Håkan Löfqvist Field Engineer
  2. 2. Neo4j, Inc. All rights reserved 2021 It’s Not What You Know
  3. 3. Neo4j, Inc. All rights reserved 2021 It’s Who You Know
  4. 4. Neo4j, Inc. All rights reserved 2021 It’s Who You Know And Where They Are
  5. 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. 6. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 6 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
  7. 7. Neo4j, Inc. All rights reserved 2021 7 7 Top 10 Tech Trends in Data and Analytics, 16 Feb 2021 According to Garner, “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 +4.8m Views on the graph algorithms short video +193k downloads
  8. 8. 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 8 Everything is Naturally Connected
  9. 9. Neo4j, Inc. All rights reserved 2021 Get More from the Data You Already Have ● Relationships already exist within your data - we help you represent them ● Find patterns, and anomalies in the global structure of your graph, or ● Add graph based features to your existing ML pipelines for more accuracy 9 Machine Learning Pipeline
  10. 10. Neo4j, Inc. All rights reserved 2021 10 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
  11. 11. Neo4j, Inc. All rights reserved 2021 11 Machine Learning Unsupervised 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 Classification Regression What’s the property value for this node? What type of node is this?
  12. 12. Neo4j, Inc. All rights reserved 2021 12 Machine Learning 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 - now in Neo4j! Node Classification Link Prediction Where will a new relationship form next? What’s the right label for this node? Community Detection Centrality Embeddings Similarity Pathfinding
  13. 13. Neo4j, Inc. All rights reserved 2021 13 Graph Algorithms in Neo4j 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
  14. 14. Neo4j, Inc. All rights reserved 2021 Graphs & Supervised Machine Learning Traditional ML problems where relationships between your data points are important predictive features 14 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
  15. 15. Neo4j, Inc. All rights reserved 2021 15 In-Graph Machine Learning 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
  16. 16. 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
  17. 17. 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 17 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
  18. 18. Neo4j, Inc. All rights reserved 2021 Our Secret Sauce: The Graph Catalog • Neo4j automates data transformations • Experiment with different data sets, data models • Fast iterations & layering • Production ready features, parallelization & enterprise support • Ability to persist and version data A graph-specific analytics workspace that’s mutable – integrated with a native-graph database Mutable In-Memory Workspace Computational Graph Native Graph Store
  19. 19. Neo4j, Inc. All rights reserved 2021 19 Neo4j: 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
  20. 20. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 20 Live Demo
  21. 21. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 21 Resources Get Started: ● Sandbox: https://neo4j.com/sandbox/ ● Guides: neo4j.com/developer/graph-data-science/ ● GitHub: github.com/neo4j/graph-data-science Graph Resources ● 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
  22. 22. Neo4j, Inc. All rights reserved 2021 22 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.

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  • SasankoSekharGantayat

    Jun. 25, 2021

Alicia Frame, Neo4j

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