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How Graphs are Changing AI
This document outlines the path of graph data science and how graphs can accelerate artificial intelligence innovation. It discusses how graphs add network structure and relationships to machine learning models, improving prediction accuracy. Graph-based techniques like feature engineering, embeddings, neural networks, and algorithms can be used to generate new predictive features, understand complex structures, and enable new forms of graph native learning. The document provides examples of applying these graph techniques to applications like fraud detection, recommendations, and churn prediction.
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Introduction to Graph Analytics and AI for enterprise data science by Amy Hodler from Neo4j.
Relationships as key behavior predictors. Highlighting financial contagion from a global to local perspective.
Graph technologies boost AI innovation. Mention of key applications: predictive maintenance, churn, fraud detection, and more.
Graphs improve prediction accuracy by utilizing existing data structures. Goals include better decisions, accuracy, and trust.
Graph data science methods include feature engineering and algorithms like Graph Neural Networks and analytics.
Using knowledge graphs for financial analytics, including credit risk and investment strategies with optimized calculations.
Graph algorithms for decision-making, querying, and financial fraud detection over extended networks.
Diverse graph algorithms for structure reasoning, including pathfinding and similarity measures for enhancing machine learning.
Integrating graph features in ML with examples from telecom churn prediction, achieving up to 89.4% accuracy.
Using graph algorithms to enhance financial fraud detection through structural connections and community identification.
Transforming graphs into feature vectors for ML. Discussing recommendations based on graph contexts.
Emphasizing graph native learning as the next AI advancement, for a more accurate understanding of data.
Summarizing the potential of graph data science. Additional resources for AI and data science provided.
Emphasizing importance of context and structure in AI, advocating Knowledge Graphs as essential tools.
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How Graphs are Changing AI
- 1. 1 Graphs & AI APath for Enterprise Data Science Amy Hodler @amyhodler Director, Graph Analytics & AI Programs Neo4j
- 3. Relationships The Strongest Predictorsof 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
- 4. Predicting Financial Contagion FromGlobal to Local 12
- 5. Graph Is AcceleratingAI 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
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- 7. Better Predictions withGraphs 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
- 8. Goals of GraphData Science Better Decisions Higher Accuracy New Learning and More Trust 16 Decision Support Graph Based Prediction Graph Native Learning
- 9. The Path ofGraph Data Science Decision Support Graph Based Prediction Graph Native Learning 17 Graph Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graphs Graph Analytics
- 10. The Path ofGraph Data Science Graph Feature Engineering Graph Embeddings Graph Neural Networks 18 Graph AnalyticsKnowledge Graphs Graph search and queries Support domain experts
- 11. Knowledge Graph withQueries Connecting the Dots has become... 19 Multiple graph layers of financial information Includes corporate data with cross-relationships and external news
- 12. Knowledge Graph withQueries 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
- 13. The Path ofGraph Data Science Graph Feature Engineering Graph Embeddings Graph Neural Networks 21 Knowledge Graphs Graph Analytics Graph queries & algorithms for offline analysis Understanding Structures
- 14. Query (e.g. Cypher/Python) Fast, localdecisioning 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
- 15. Deceptively Simple Queries Howmany 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
- 16. Graph Analytics viaAlgorithms 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
- 17. • 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.
- 18. The Path ofGraph Data Science Graph Embeddings Graph Neural Networks 26 Knowledge Graphs Graph Analytics Graph Feature Engineering Graph algorithms & queries for machine learning Improve Prediction Accuracy
- 19. Graph Feature Engineering FeatureEngineering 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
- 20. Feature Engineering usingGraph 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?
- 21. 30 Feature Engineering usingGraph 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
- 22. 31 Feature Engineering usingGraph 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
- 23. Feature Engineering usingGraph 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
- 24. The Path ofGraph 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
- 25. Embedding transforms graphsinto 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
- 26. Graph Embeddings RECOMMENDATIONS ExplainableReasoning 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
- 27. The Path ofGraph Data Science Graph Feature Engineering Graph Embeddings 36 Knowledge Graphs Graph Analytics Graph Neural Networks ML within a Graph New learning methods
- 28. “Graphs bring anability to generalize about structure that the individual neural nets don't have.” don't have.” Next Major Advancement in AI: Graph Native Learning
- 29. Next Major Advancementin 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
- 30. The Path ofGraph Data Science Decision Support Graph Based Prediction Graph Native Learning 39 Graph Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graphs Graph Analytics
- 31. Resources Business – AIWhitepaper neo4j.com/use-cases/ artificial-intelligence-analytics/ Data Scientists neo4j.com/sandbox Developers neo4j.com/download neo4j.com/graph-algorithms-book
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- 33. 43 “AI is notall 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
- 34. 44 Graphs & AI APath for Enterprise Data Science Amy Hodler @amyhodler Director, Graph Analytics & AI Programs Neo4j
- 35. Graph Data Science takeyour analytics one step further 45