How Graph Technology is Changing AI
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Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible. Join us to hear about 4 areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. We'll provide examples and specifically look how: - Graphs provide better accuracy through connected feature extraction - Graphs provide better performance through contextual model optimization - Graphs provide context through knowledge graphs - Graphs add explainability to neural networks Speakers: Jake Graham, Alicia Frame
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How Graph Technology is Changing AI
- 1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
- 2. Jake Graham & Alicia Frame, Neo4j How Graph Technology is Changing AI #UnifiedAnalytics #SparkAISummit
- 4. FinCrime Detection Drug Discovery Recommendations Cybersecurity Predictive Maintenance Customer Segmentation Churn Prediction Search/MDM Where Do Graphs Matter?
- 5. CAR DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Latitude: 37.5629900° Longitude: -122.3255300° Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes MARRIED TO LIVES WITH OW NS PERSON PERSON 5 Labeled Property Graphs
- 6. Graphs provide more accurate predictions With the data you already have o Current data science models ignore network structure and complex relationships o Graph models add highly predictive features to existing ML models MACHINE LEARNING LIBRARY
- 7. 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. Lest you think the authors think they've got it all figured out, the paper lists some lingering shortcomings. Battaglia et al. pose the big question, "Where do the graphs come from that graph networks operate over?”
- 8. SparkCypher & SparkGraph Neo4j Morpheus Neo4j Graph Platform Cypher 9 in Spark to create non- persistent graphs Cypher 10 over Spark for seamless Neo4j integration Native Graph Algorithms, Processing, and Storage Graph Model Building
- 9. Explore Graphs Build Graphs o Massively scalable o Powerful data pipelining o Robust ML Libraries o Non-persistent, non-native graphs o Persistent, dynamic graphs o Graph native query and algorithm performance o Constantly growing list of graph algorithms and embeddings in in
- 10. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 11. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 12. Connecting the Dots at NASA “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
- 13. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 14. Mining Knowledge Graphs for Drug Discovery • 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 14
- 15. Knowledge Graphs - het.io • 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 15
- 16. Knowledge Graphs - het.io • 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 16
- 17. Knowledge Graphs: getting started 17 Graph Transactions Graph Analytics • Build a graph data pipeline to bring into native graph • Bring graph features back to ML pipeline • Move to Neo4J to build expert queries and persist your graph • Merge distributed data into dataframes • Reshape your tables into graphs • Explore cypher queries SparkCypher & SparkGraph Neo4j Morpheus Neo4j Graph Platform
- 18. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 19. Graph Feature Engineering 19 MACHINE LEARNING LIBRARY Make use of your existing machine learning pipeline: • Tabular data from Spark • Enriched with graph based features from Neo4j • Combined into a single model building pipeline
- 20. Categories of Graph Features 20 Pathfinding & Search Centrality / Importance Community Detection Heuristic Link Prediction Finds optimal paths or evaluates route availability and quality Determines the importance of distinct nodes in the network Detects group clustering or partition options Evaluates how alike nodes are Estimates the likelihood of nodes forming a relationship SimilarityEmbeddings Vectors that capture connectivity or topology
- 21. Financial Crime: Detecting Fraud 21 Many large financial institutions have existing pipelines to identify fraud Graph based features improve accuracy: • Connected components to identify disjoint graphs • PageRank to measure influence • Louvain to identify communities • Jaccard to measure account similarity
- 22. Financial Crime: Detecting Fraud 22 Many large financial institutions have existing pipelines to identify fraud Graph based features improve accuracy: • Connected components to identify disjoint graphs • PageRank to measure influence • Louvain to identify communities • Jaccard to measure account similarity
- 23. Graph Feature Engineering: getting started 23 Graph Transactions Graph Analytics • Move to Neo4J to build run native graph algorithms • Write algorithm derived features to persistent graph • Merge distributed data into dataframes • Reshape your tables into graphs • Explore graph algorithms • Build a graph data pipeline to bring into native graph • Bring graph features back to ML pipeline
- 24. Graph Features in Neo4J 24 • 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
- 25. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 26. Graph Embeddings Embeddings transform graphs into a vector, or set of vectors, describing topology, connectivity, or attributes of nodes and edges in the graph 26 • Vertex embeddings: describe connectivity of each node • Path embeddings: traversals across the graph • Graph embeddings: encode an entire graph into a single vector
- 27. Graph Embeddings - Recommendations Explainable Reasoning over Knowledge Graphs for Recommendation 27
- 28. Graph Embeddings - Recommendations Explainable Reasoning over Knowledge Graphs for Recommendation 28
- 29. Graph Embeddings: Getting Started 29 Graph Transactions Graph Analytics • Move to Neo4J to build expert queries and persist • Stay tuned for DeepWalk and DeepGL • Merge distributed data into dataframes • Reshape your tables into graphs • Explore graph algorithms • Build a graph data pipeline to bring into native graph • Bring graph features back to ML pipeline
- 30. Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 31. Graph Native Learning Deep Learning refers to training multi-layer neural networks using gradient descent 31
- 32. Graph Native Learning Graph Native Learning refers to deep learning models that take a graph as an input, performs computations, and returns a graph. 32 Battaglia et al, 2018
- 33. Graph Native Learning Example: electron path prediction Bradshaw et al, 2019 33 Given reactants and reagents, what will the products be? Given reactants and reagents, what will the products be?
- 34. Graph Native Learning Example: electron path prediction 34
- 35. 35#UnifiedAnalytics #SparkAISummit Query Based Knowledge Graph Query Based Feature Engineering Graph Algorithm Feature Engineering Graph Embeddings Graph Neural Networks Knowledge Graph Graph Feature Engineering Graph Native Learning Neo4J for Graph Persistence Delivery Timeline Complexity The Steps of Graph Data Science
- 36. Resources o O’Reilly Graph Algorithms Book o Neo4j Graph Algorithms Library o Check out the documentation o Reach out to us 36#UnifiedAnalytics #SparkAISummit
- 37. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT