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Chapters

This course contains 26 chapters covering 259 concepts in graph data modeling.

Part 1: Foundations

  1. Introduction to Graph Data Modeling - Why graph data modeling matters in the age of AI
  2. Graph Fundamentals - Core structures: nodes, edges, properties, and paths

Part 2: Domain Modeling Essentials

  1. Modeling Customers - Individual, household, and corporate customer models
  2. Modeling Products - Product taxonomies, similarity, and metadata
  3. Modeling Space - Geographic locations, regions, and spatial algorithms
  4. Modeling Time - DateTime structures, calendars, and time hierarchies

Part 3: Knowledge and Language

  1. Knowledge Graphs and Concepts - Ontologies, taxonomies, and semantic structures
  2. Modeling Language - NLP, documents, and linguistic relationships

Part 4: Industry Applications

  1. Fraud Detection - Detecting fraud, waste, abuse, and money laundering
  2. Healthcare Modeling - Patients, providers, clinical data, and FHIR

Part 5: Advanced Techniques

  1. Entity Resolution - Connecting data through similarity and matching
  2. Digital Twins - Real-time models of physical systems
  3. Scene Graphs - Visual scene understanding and robotics

Part 6: Rules and Code

  1. Modeling Rules - Business rules, workflows, and decision trees
  2. Modeling Code - Code graphs, call graphs, and static analysis
  3. Security Threat Modeling - Networks, vulnerabilities, and access control

Part 7: Processes and Learning

  1. Process and Event Modeling - Events, workflows, and dashboards
  2. Learning Systems - Learning graphs, paths, and recommendations

Part 8: Advanced Analytics

  1. Causality Modeling - Causal graphs, systems thinking, and Bayesian networks
  2. Lineage and Provenance - Tracking data origins and transformations
  3. Metadata Modeling - Data governance and schema management
  4. Supply Chain Modeling - Inventory, suppliers, and transportation

Part 9: Temporal Modeling

  1. Bitemporal Modeling - Real-world time vs. system time

Part 10: Evolution and Future

  1. Model Evolution - Tradeoffs, complexity, and sustainability
  2. Brain Architecture - Neural models and the 1000 Brains Theory
  3. AI and Graph Futures - The convergence of graphs and artificial intelligence