Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Neo4j)
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Knowledge Graphs and Graph Data Science: More Context, Better Predictions
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Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Neo4j)
- 1. Neo4j, Inc. All rights reserved 2022 Knowledge Graphs & Graph Data Science: More Context, Better Predictions Dr. J Barrasa Sr. Director SA jesus.barrasa@neo4j.com
- 2. Neo4j, Inc. All rights reserved 2022 Data Without Relationships Has Little Context It’s not just the raw data, it’s the relationships behind them
- 3. Neo4j, Inc. All rights reserved 2022 Graphs & Data Science Graph-Native ML Graph Algorithms Graph Queries Knowledge Graph 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 graph-native supervised ML models to predict links, labels, and missing data.
- 4. Neo4j, Inc. All rights reserved 2022 Building A Knowledge Graph Data Graph Knowledge Graph Semantics Relationships
- 5. Neo4j, Inc. All rights reserved 2022 Rich (and complex) Data A B C D E A B C D E One-to-Many Relationships Across Many Entities Small, Wide Data Hierarchical & Recursive Data Many-to-Many Relationships Nested Tree Structures Recursion (Self-Joins) Deep Hierarchies Link Inference (If C relates to A and A relates to E, then C must relate to E) Node Similarity Hidden Data Legacy Data Frozen Data Legacy SQL Systems Data Lake Fact Tables Graph Data Science - Machine Reasoning A C E
- 6. Neo4j, Inc. All rights reserved 2022 Semantics Taxonomies Thesaurus Ontology Controlled vocab
- 7. Neo4j, Inc. All rights reserved 2022 Graph Query Hey, knowledge graph! Tell me “which genes regulate which pathways”. MATCH path=(g:Gene)-[r:REGULATES]->(p:Pathway) RETURN path
- 8. Neo4j, Inc. All rights reserved 2022 Graph Data Science Answers the Big Questions What’s important? What’s unusual? What’s going to happen next? But traditional approaches to data make it impossible to reveal and effectively use those connections as data sizes become large Predictive signals get lost in big data noise Context is Powerful Graph Data Science Uses Context to Answer Critical Questions
- 9. Neo4j, Inc. All rights reserved 2022 Graph Catalog • Single API to materialize graph projections • Subset, reshape and transform your underlying database • Modify projections with mutate • Graph Catalog manages your projections • (new!) Backup, restore, or export projected graphs We transform your underlying data store into a highly optimized CSR representation, built for massively parallel data science workloads. Mutable In-Memory Workspace Computational Graph Native Graph Store
- 10. Neo4j, Inc. All rights reserved 2022 Graph Algorithms Pathfinding & Search • A* Shortest Path • All Pairs Shortest Path • Breadth & Depth First Search • Delta-Stepping Single-Source • Dijkstra Single-Source • Dijkstra Source-Target • K-Spanning Tree (MST) • Minimum Weight Spanning Tree • Random Walk • Yen’s K Shortest Path Centrality & Importance • ArticleRank • Betweenness Centrality & Approx. • Closeness Centrality • Degree Centrality • Eigenvector Centrality • Harmonic Centrality • Hyperlink Induced Topic Search (HITS) • Influence Maximization (Greedy, CELF) • PageRank • Personalized PageRank Community Detection • Conductance Metric • K-1 Coloring • K-Means Clustering • Label Propagation • Leiden Algorithm • Local Clustering Coefficient • Louvain Algorithm • Max K-Cut • Modularity Optimization • Speaker Listener Label Propagation • Strongly Connected Components • Triangle Count • Weakly Connected Components Heuristic Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors Similarity • K-Nearest Neighbors (KNN) • Node Similarity • Filtered KNN & Node Similarity • Cosine & Pearson Similarity Functions • Euclidean Distance Similarity Function • Euclidean Similarity Function • Jaccard & Overlap Similarity Functions Graph Embeddings • Fast Random Projection (FastRP) • FastRP with Property Weights • GraphSAGE • Node2Vec
- 11. Neo4j, Inc. All rights reserved 2022 Machine Learning Pipelines Node classification: “What label should this node have?” Link prediction: “Is there a relationship between these nodes?” Labeled data: Pairs of nodes that are either linked or not Features: Pre-existing attributes, algorithm results, embeddings Property Regression (new!) “What’s the value for this missing property?” We discover the best model for you - you just supply the data! Persist and Publish for Production
- 12. Neo4j, Inc. All rights reserved 2022 The Drug Lifecycle Pipeline DISCOVERY DEVELOPMENT COMMERCIALIZATION A Knowledge Graph for Reaction and Synthesis Prediction Target Discovery Hit Generation Lead Identification Lead Optimization Animal Models Clinical Trials FDA/EMA Review & Approval Post Approval Manufacturing
- 13. Neo4j, Inc. All rights reserved 2022 The Drug Lifecycle Pipeline DISCOVERY DEVELOPMENT COMMERCIALIZATION A Knowledge Graph for Reaction and Synthesis Prediction Target Discovery Hit Generation Lead Identification Lead Optimization Animal Models Clinical Trials FDA/EMA Review & Approval Post Approval Manufacturing End-to-End Drug Discovery Project Management Using a Knowledge Graph
- 14. Neo4j, Inc. All rights reserved 2022 The Drug Lifecycle Pipeline DISCOVERY DEVELOPMENT COMMERCIALIZATION A Knowledge Graph for Reaction and Synthesis Prediction How Will Knowledge Graphs Improve Clinical Reporting Workflows Target Discovery Hit Generation Lead Identification Lead Optimization Animal Models Clinical Trials FDA/EMA Review & Approval Post Approval Manufacturing End-to-End Drug Discovery Project Management Using a Knowledge Graph
- 15. Neo4j, Inc. All rights reserved 2022 The Drug Lifecycle Pipeline DISCOVERY DEVELOPMENT COMMERCIALIZATION A Knowledge Graph for Reaction and Synthesis Prediction How Will Knowledge Graphs Improve Clinical Reporting Workflows Target Discovery Hit Generation Lead Identification Lead Optimization Animal Models Clinical Trials FDA/EMA Review & Approval Post Approval Manufacturing Tracking Drug Genealogy Efficiently with Knowledge Graphs End-to-End Drug Discovery Project Management Using a Knowledge Graph
- 16. Neo4j, Inc. All rights reserved 2022 The Drug Lifecycle Pipeline DISCOVERY DEVELOPMENT COMMERCIALIZATION A Knowledge Graph for Reaction and Synthesis Prediction How Will Knowledge Graphs Improve Clinical Reporting Workflows Target Discovery Hit Generation Lead Identification Lead Optimization Animal Models Clinical Trials FDA/EMA Review & Approval Post Approval Manufacturing Tracking Drug Genealogy Efficiently with Knowledge Graphs End-to-End Drug Discovery Project Management Using a Knowledge Graph
- 17. Neo4j, Inc. All rights reserved 2022 Thank you! Contact: jesus.barrasa@neo4j.com 2