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How Graphs are Changing AI

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Speaker: Amy Hodler, Neo4j

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How Graphs are Changing AI

  1. 1. 1 Graphs & AI A Path for Enterprise Data Science Amy Hodler @amyhodler Director, Graph Analytics & AI Programs Neo4j
  2. 2. Relationships The Strongest Predictors of Behavior! “Increasingly we're learning that you can make better predictions about people by getting all the information from their friends and their friends’ friends than you can from the information you have about the person themselves” James Fowler 11
  3. 3. Predicting Financial Contagion From Global to Local 12
  4. 4. Graph Is Accelerating AI Innovation 13 4,000 3,000 2,000 1,000 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 Graph Technology Mentioned graph neural network graph convolutional graph embedding graph learning graph attention graph kernel graph completion AI Research Papers Featuring Graph Source: Dimension Knowledge System
  5. 5. Predictive Maintenance Churn Prediction Fraud Detection Life SciencesRecommendations Cybersecurity Customer Segmentation Search/MDM Graph Data Science Applications
  6. 6. Better Predictions with Graphs Using the Data You Already Have • Current data science models ignore network structure • Graphs add highly predictive features to ML models, increasing accuracy • Otherwise unattainable predictions based on relationships Machine Learning Pipeline 15
  7. 7. Goals of Graph Data Science Better Decisions Higher Accuracy New Learning and More Trust 16 Decision Support Graph Based Prediction Graph Native Learning
  8. 8. The Path of Graph Data Science Decision Support Graph Based Prediction Graph Native Learning 17 Graph Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graphs Graph Analytics
  9. 9. The Path of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Neural Networks 18 Graph AnalyticsKnowledge Graphs Graph search and queries Support domain experts
  10. 10. Knowledge Graph with Queries Connecting the Dots has become... 19 Multiple graph layers of financial information Includes corporate data with cross-relationships and external news
  11. 11. Knowledge Graph with Queries Connecting the Dots Dashboards and tools • Credit risk • Investment risk • Portfolio news recommendations • Typical analyst portfolio is 200 companies • Custom relative weights 1 Week Snapshot: 800,000 shortest path calculations for the ranked newsfeed. Each calculation optimized to take approximately 10 ms. has become... 20
  12. 12. The Path of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Neural Networks 21 Knowledge Graphs Graph Analytics Graph queries & algorithms for offline analysis Understanding Structures
  13. 13. Query (e.g. Cypher/Python) Fast, local decisioning and pattern matching Graph Algorithms (e.g. Neo4j library, GraphX) Global analysis and iterations You know what you’re looking for and making a decision You’re learning the overall structure of a network, updating data, and predicting Local Patterns Global Computation 22
  14. 14. Deceptively Simple Queries How many flagged accounts are in the applicant’s network 4+ hops out? How many login / account variables in common? Add these metrics to your approval process Difficult for RDMS systems over 3 hops Graph Analytics via Queries Detecting Financial Fraud Improving existing pipelines to identify fraud via heuristics 23
  15. 15. Graph Analytics via Algorithms Generally Unsupervised 24 A subset of data science algorithms that come from network science, Graph Algorithms enable reasoning about network structure. Pathfinding and Search Centrality (Importance) Community Detection Heuristic Link Prediction Similarity
  16. 16. • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Overlap Similarity • Pearson Similarity • Approximate KNN • Degree Centrality • Closeness Centrality • CC Variations: Harmonic, Dangalchev, Wasserman & Faust • Betweenness Centrality • Approximate Betweenness Centrality • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • Balanced Triad (identification) +45 Graph Algorithms in Neo4j • Parallel Breadth First Search • Parallel Depth First Search • 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) • Random Walk • Degree Centrality • Closeness Centrality • CC Variations: Harmonic, Dangalchev, Wasserman & Faust • Betweenness Centrality • Approximate Betweenness Centrality • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • Balanced Triad (identification) • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Overlap Similarity • Pearson Similarity • Approximate KNN Pathfinding & Search Centrality / Importance Community Detection Similarity Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors25 There is significant demand for graph algorithms. Neo4j will be the first enterprise grade way to run them.
  17. 17. The Path of Graph Data Science Graph Embeddings Graph Neural Networks 26 Knowledge Graphs Graph Analytics Graph Feature Engineering Graph algorithms & queries for machine learning Improve Prediction Accuracy
  18. 18. Graph Feature Engineering Feature Engineering is how we combine and process the data to create new, more meaningful features, such as clustering or connectivity metrics. Graph features add more dimensions to machine learning EXTRACTION 27
  19. 19. Feature Engineering using Graph Queries Telecom-churn prediction Churn prediction research has found that simple hand-engineered features are highly predictive • How many calls/texts has an account made? • How many of their contacts have churned?
  20. 20. 30 Feature Engineering using Graph Queries Telecom-churn prediction Add connected features based on graph queries to tabular data Raw Data: Call Detail Records Input Data: CDR Sample Call Stats by: Incoming Outgoing Per day Short durations In-network Centrality SMS’s … Test/Training Data Caller ID Receiver ID Time Duration Location … Caller ID Receiver ID Time Duration Location … Identify Early Predictors: Select simple, interpretable metrics that are highly correlated w/churn Churn Score: Supervised learning to predict binary & continuous measures of churn Output/Results Random Sample Selection Feature Engineering
  21. 21. 31 Feature Engineering using Graph Queries Telecom-churn prediction 89.4% Accuracy in Subscriber Churn Prediction Raw Data: Call Detail Records Input Data: CDR Sample Call Stats by: Incoming Outgoing Per day Short durations In-network Centrality SMS’s … Test/Training Data Caller ID Receiver ID Time Duration Location … Caller ID Receiver ID Time Duration Location … Identify Early Predictors: Select simple, interpretable metrics that are highly correlated w/churn Churn Score: Supervised learning to predict binary & continuous measures of churn Output/Results Random Sample Selection Feature Engineering Source: Behavioral Modeling for Churn Prediction by Khan et al, 2015
  22. 22. Feature Engineering using Graph Algorithms Detecting Financial Fraud Using Structure to Improve ML Predictions Connected components identify disjointed group sharing identifiers PageRank to measure influence and transaction volumes Louvain to identify communities that frequently interact Jaccard to measure account similarity
  23. 23. The Path of Graph Data Science Graph Feature Engineering Graph Neural Networks 33 Knowledge Graphs Graph Analytics Graph Embeddings Graph embedding algorithms for ML features Predictions on complex structures
  24. 24. Embedding transforms graphs into a feature vector, or set of vectors, describing topology, connectivity, or attributes of nodes and relationships in the graph Graph Embeddings • Node embeddings: describe connectivity of each node • Path embeddings: traversals across the graph • Graph embeddings: encode an entire graph into a single vector Phases of Deep Walk Approach 34
  25. 25. Graph Embeddings RECOMMENDATIONS Explainable Reasoning over Knowledge Graphs for Recommendations 35 Pop Folk Castle on the Hill ÷ Album Ed Sheeran I See FireTony Shape of You SungBy IsSingerOf Interact Produce WrittenBy Derek Recommendations for Derek 0.06 0.24 0.24 0.26 0.03 0.30 .63
  26. 26. The Path of Graph Data Science Graph Feature Engineering Graph Embeddings 36 Knowledge Graphs Graph Analytics Graph Neural Networks ML within a Graph New learning methods
  27. 27. “Graphs bring an ability to generalize about structure that the individual neural nets don't have.” don't have.” Next Major Advancement in AI: Graph Native Learning
  28. 28. Next Major Advancement in AI: Graph Native Learning 38 Implements machine learning in a graph environment Input data as a graph Learns while preserving transient states Output as a graph Track and validate AI decision paths More accurate with less data and training
  29. 29. The Path of Graph Data Science Decision Support Graph Based Prediction Graph Native Learning 39 Graph Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graphs Graph Analytics
  30. 30. Resources Business – AI Whitepaper neo4j.com/use-cases/ artificial-intelligence-analytics/ Data Scientists neo4j.com/sandbox Developers neo4j.com/download neo4j.com/graph-algorithms-book
  31. 31. One Thing
  32. 32. 43 “AI is not all about Machine Learning. Context, structure, and reasoning are necessary ingredients, and Knowledge Graphs and Linked Data are key technologies for this.” Wais Bashir Managing Editor, Onyx Advisory
  33. 33. 44 Graphs & AI A Path for Enterprise Data Science Amy Hodler @amyhodler Director, Graph Analytics & AI Programs Neo4j
  34. 34. Graph Data Science take your analytics one step further 45

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