Table of Contents

  1. Data Representation as Graphs – Introducing Neo4j
  2. Processing Graphs with Cypher Queries
  3. A Peek into Recommendation Engines and Knowledge Graphs
  4. Effective Graph Traversal and the GDS Library
  5. Centrality Metrics, PageRank, and Fraud Detection
  6. Understanding Similarity and Cluster Analysis Algorithms
  7. Applications of Graphs to Machine Learning
  8. Link Prediction with Neo4j 
  9. Embedding, Neural Nets, and LLMs with Graphs
  10. Profiling, Optimizing, and running Neo4j and GDS in Production