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Graphs for artificial intelligence

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- 1. 1 Leveraging Graphs for Better AI Alicia Frame Senior Data Scientist, neo4j alicia.frame@neo4j.com Washington DC, May 2019
- 2. Financial Services Drug Discovery Recommendations Cybersecurity Predictive Maintenance Customer Segmentation Churn Prediction Search/MDM Graph Data Science Applications
- 3. • Current data science models ignore network structure • Graphs add highly predictive features to existing ML models • Otherwise unattainable predictions based on relationships Novel & More Accurate Predictions with the Data You Already Have Machine Learning Pipeline
- 4. “The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.” "Where do the graphs come from that graph networks operate over?”
- 5. Building a Graph ML Model Data Sources Native Graph Platform Machine Learning Aggregate Disparate Data and Cleanse Build Predictive ModelsUnify Graphs and Engineer Features Parquet JSON and more… MLlib and more…
- 6. Spark Graph Native Graph Platform Machine Learning Example: Spark & Neo4j Workflow Graph Transactions Graph Analytics Cypher 9 in Spark 3.0 to create non-persistent graphs MLlib to Train Models Native Graph Algorithms, Processing, and Storage
- 7. Explore Graphs Build Graph Solutions • Massively scalable • Powerful data pipelining • Robust ML Libraries • Non-persistent, non-native graphs • Persistent, dynamic graphs • Graph native query and algorithm performance • Constantly growing list of graph algorithms and embeddings
- 8. The Steps of Graph Data Science Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Enterprise Maturity DataScienceComplexity Knowledge Graphs Graph Feature Engineering Graph Native Learning Graph Persistence
- 9. Steps Forward in Graph Data Science Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Enterprise Maturity DataScienceComplexity
- 10. Query-Based Knowledge Graphs Connecting the Dots • Many connected data sources: corporate data with cross- relationships, external news, and customized weighting • Dashboards and tools • Credit risk • Investment risk • Portfolio news recommendations
- 11. Steps Forward in Graph Data Science Query Based Knowledge Graph Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Query Based Feature Engineering Enterprise Maturity DataScienceComplexity
- 12. HetioNet is a knowledge graph integrating over 50 years of biomedical data Leveraged to predict new uses for drugs by using the graph topology to create features to predict new links Query-Based Feature Engineering Mining Data for Drug Discovery
- 13. HetioNet is a knowledge graph integrating over 50 years of biomedical data Leveraged to predict new uses for drugs by using the graph topology to create features to predict new links Query-Based Feature Engineering Mining Data for Drug Discovery
- 14. HetioNet is a knowledge graph integrating over 50 years of biomedical data Leveraged to predict new uses for drugs by using the graph topology to create features to predict new links Query-Based Feature Engineering Mining Data for Drug Discovery
- 15. Spark Graph Native Graph Platform Machine Learning • Merge distributed data into DataFrames • Reshape your tables into graphs • Explore cypher queries • Move to Neo4j to build expert queries • Persist your graph Knowledge Graphs: Getting Started Example with Spark • Bring query based graph features to ML pipeline Graph Transactions Graph Analytics
- 16. Steps Forward in Graph Data Science Query Based Feature Engineering Graph Embeddings Graph Neural Networks Query Based Knowledge Graph Graph Algorithm Feature Engineering Enterprise Maturity DataScienceComplexity
- 17. Feature Engineering is how we combine and process the data to create new, more meaningful features, such as clustering or connectivity metrics. Graph Feature Engineering Add More Descriptive Features: - Influence - Relationships - Communities
- 18. 27 Graph Feature Categories & Algorithms Pathfinding & Search Finds the optimal paths or evaluates route availability and quality Centrality / Importance Determines the importance of distinct nodes in the network Community Detection Detects group clustering or partition options Heuristic Link Prediction Estimates the likelihood of nodes forming a relationship Evaluates how alike nodes are Similarity Embeddings Learned representations of connectivity or topology
- 19. • Connected components to identify disjointed graphs sharing identifiers • PageRank to measure influence and transaction volumes • Louvain to identify communities that frequently interact • Jaccard to measure account similarity based on relationships 28 Financial Crime: Detecting Fraud Large financial institutions already have existing pipelines to identify fraud via heuristics and models Graph based features improve accuracy:
- 20. +48,000 U.S. Patents for Graph Fraud / Anomaly Detection in the last 10 years
- 21. Spark Graph Native Graph Platform Machine Learning • Merge distributed data into DataFrames • Reshape your tables into graphs • Explore cypher queries and simple algorithms • Persist your graph • Create rule based features • Run native graph algorithms and write to graph or stream Graph Feature Engineering: Getting Started Example with Spark • Bring graph features to ML pipeline for training Graph Transactions Graph Analytics
- 22. 31 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 – 1 Step & Multi-Step • Balanced Triad (identification) • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Overlap Similarity • Pearson Similarity Pathfinding & Search Centrality / Importance Community Detection Similarity neo4j.com/docs/ graph-algorithms/current/ Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors
- 23. Steps Forward in Graph Data Science Query Based Knowledge Graph Graph Algorithm Feature Engineering Graph Neural Networks Query Based Feature Engineering Graph Embeddings Enterprise Maturity DataScienceComplexity
- 24. Embedding transforms graphs into a vector, or set of vectors, describing topology, connectivity, or attributes of nodes and edges in the graph 33 Graph Embeddings • Vertex embeddings: describe connectivity of each node • Path embeddings: traversals across the graph • Graph embeddings: encode an entire graph into a single vector
- 25. Explainable Reasoning over Knowledge Graphs for Recommendation 34 Graph Embeddings - Recommendations
- 26. 35 Graph Embeddings - Recommendations Explainable Reasoning over Knowledge Graphs for Recommendation
- 27. Spark Graph Native Graph Platform Machine Learning • Merge distributed data into DataFrames • Reshape your tables into graphs • Explore cypher queries and simple algorithms • Move to Neo4j to build expert queries • Write to persist • Stay tuned for DeepWalk and DeepGL algorithms Graph Feature Engineering: Getting Started Example with Spark • Bring graph features to ML pipeline for training Graph Transactions Graph Analytics
- 28. Steps Forward in Graph Data Science Query Based Knowledge Graph Graph Algorithm Feature EngineeringQuery Based Feature Engineering Graph Neural Networks Graph Embeddings Enterprise Maturity DataScienceComplexity
- 29. Deep Learning refers to training multi-layer neural networks using gradient descent 39 Graph Native Learning
- 30. Graph Native Learning refers to deep learning models that take a graph as an input, performs computations, and return a graph 40 Graph Native Learning Battaglia et al, 2018
- 31. Example: electron path prediction Bradshaw et al, 2019 41 Graph Native Learning Given reactants and reagents, what will the products be? Given reactants and reagents, what will the products be?
- 32. Example: electron path prediction 42 Graph Native Learning
- 33. Progressing in Graph Data Science Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Enterprise Maturity DataScienceComplexity Knowledge Graphs Graph Feature Engineering Graph Native Learning Graph Persistence
- 34. Resources Business • neo4j.com/use-cases/ artificial-intelligence-analytics/ Data Scientists/Developers • neo4j.com/sandbox • neo4j.com/developer/ • community.neo4j.com alicia.frame@neo4j.com @aliciaframe1 neo4j.com/ graph-algorithms-book
- 35. 47 EXTRA STUFF
- 36. 49 Example: electron path prediction Bradshaw et al, 2019 Graph Native Learning Predicting Chemical Reactions
- 37. Example: electron path prediction Bradshaw et al, 2019 50 Graph Native Learning Predicting Chemical Reactions Given reactants and reagents, what will the products be?
- 38. Thomson Reuters Graph 51 • Data Fusion for Portfolio Managers • Graph layers
- 39. Software Financial Services Telecom Retail & Consumer Goods Media & Entertainment Other Industries Airbus 300 Enterprises & 10k’s Projects on Neo4j
- 40. Query-Based Knowledge Graphs Connecting the Dots “Using Neo4j someone from our Orion project found information from the Apollo project that prevented an issue, saving well over two years of work and one million dollars of taxpayer funds.” David Meza, Chief Knowledge Architect – NASA 2015

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