πŸ•ΈοΈ
Graph Engineering
13 Parts Β· 44 Chapters
πŸ—ΊοΈTerritory Mapβ–Ύ
Ch 0 β€” The Map of Graph Engineering
🎯⚑
πŸ”΅Graph Foundationsβ–Ύ
Ch 1 β€” What Is a Graph?
🎯⚑
Ch 2 β€” Graph Representations
⚑
Ch 3 β€” Graph Properties
Ch 4 β€” Graph Taxonomy
🧭Core Algorithmsβ–Ύ
Ch 5 β€” BFS & DFS
🎯⚑
Ch 6 β€” Shortest Paths
🎯⚑
Ch 7 β€” Centrality & Influence
🎯⚑
Ch 8 β€” Community Detection
⚑
Ch 9 β€” Graph Coloring & Matching
Ch 37 β€” Minimum Spanning Trees
🎯
Ch 38 β€” Strongly Connected Components
🎯
Ch 39 β€” Topological Sort
🎯
Ch 40 β€” Max-Flow / Min-Cut
🎯
πŸ—„οΈGraph Databasesβ–Ύ
Ch 10 β€” Property Graphs
⚑
Ch 11 β€” Cypher & Gremlin
⚑
Ch 12 β€” Graph Storage Engines
πŸ•ΈοΈKnowledge Graphsβ–Ύ
Ch 13 β€” Knowledge Graphs
🎯⚑
Ch 14 β€” Ontologies & Schema
Ch 15 β€” Graph Embeddings
⚑
🧠Graph Neural Networksβ–Ύ
Ch 16 β€” Graph Neural Networks
🎯⚑
Ch 17 β€” GNN Architecture Patterns
Ch 18 β€” GNN Applications
Ch 19 β€” GNN Training at Scale
πŸ”Graph RAG & Retrievalβ–Έ
βš™οΈGraph Analytics at Scaleβ–Έ
πŸ€–Graphs in AI Systemsβ–Έ
πŸš€Engineering Frontierβ–Έ
🧩Compiler & Program Graphsβ–Έ
🏒Organizational Memory Graphsβ–Έ
πŸ“Theory Foundationsβ–Έ
Chapters read0/44
πŸ•ΈοΈ Learning roadmap

Graph Engineering

A visual route from nodes and edges to graph-native AI, compilers, and organizational memory.

13 parts44 chapters73 terms
πŸ•ΈοΈ
NodesPathsKGGNNAI
Nodes β†’ algorithms β†’ intelligence
Version 1.0 Β· 2026

13 Parts Β· 44 Chapters Β· From Nodes and Edges to Graph-Native AI

Learning roadmap
Engineering Pathway
Beginner
Intermediate
Advanced
Expert
Elite
Territory Map
Graph Foundations
Core Algorithms
Graph Databases
Knowledge Graphs
Graph Neural Networks
Graph RAG & Retrieval
Graph Analytics at Scale
Graphs in AI Systems
Engineering Frontier
Compiler & Program Graphs
Organizational Memory Graphs
Theory Foundations
BeginnerIntermediateAdvancedExpertElite0Start12345678910111213141516171819202122232425262728293031323334353637383940414243
44
Chapters
13
Parts
21
Quizzes
73
Terms
πŸ“ Choose a Learning Path
Beginner Path
Ch 0 β†’ Foundations β†’ Core algorithms
Engineer Track
Storage, query, production graph systems
AI/ML Route
GNNs, embeddings, graph RAG, graph AI
Strategy View
Trade-offs, frontier, infrastructure
πŸ—ΊοΈPart 0 β€” Territory Map
⚑🎯
Ch 0
The Map of Graph Engineering
From nodes and edges to graph-native AI systems
πŸ”΅Part 1 β€” Graph Foundations
⚑🎯
Ch 1
What Is a Graph?
Nodes, edges, and the mathematics of connection
⚑
Ch 2
Graph Representations
Adjacency lists, matrices, and edge lists β€” each with trade-offs
Ch 3
Graph Properties
Degree, density, connectivity, diameter β€” the vital signs of a graph
Ch 4
Graph Taxonomy
Trees, DAGs, cycles, cliques β€” named structures with special properties
🧭Part 2 β€” Core Algorithms
⚑🎯
Ch 5
BFS & DFS
The two fundamental traversals β€” breadth-first and depth-first
⚑🎯
Ch 6
Shortest Paths
Dijkstra, Bellman-Ford, A* β€” finding the cheapest route
⚑🎯
Ch 7
Centrality & Influence
Who matters in a network? Degree, betweenness, PageRank
⚑
Ch 8
Community Detection
Finding clusters, modules, and communities in graphs
Ch 9
Graph Coloring & Matching
Scheduling, assignment, and resource allocation on graphs
🎯
Ch 37
Minimum Spanning Trees
Kruskal, Prim, BorΕ―vka β€” connecting everything for the least cost
🎯
Ch 38
Strongly Connected Components
Tarjan and Kosaraju β€” collapsing cycles into supernodes
🎯
Ch 39
Topological Sort
Linearizing a DAG so every dependency comes first
🎯
Ch 40
Max-Flow / Min-Cut
Ford–Fulkerson, Edmonds–Karp, push–relabel β€” the backbone of matching and scheduling
πŸ—„οΈPart 3 β€” Graph Databases
⚑
Ch 10
Property Graphs
Nodes and edges with arbitrary key-value properties
⚑
Ch 11
Cypher & Gremlin
Declarative and imperative graph query languages
Ch 12
Graph Storage Engines
Native graph vs relational vs columnar β€” where do you put the edges?
πŸ•ΈοΈPart 4 β€” Knowledge Graphs
⚑🎯
Ch 13
Knowledge Graphs
Entities, relations, and ontologies β€” structured world knowledge
Ch 14
Ontologies & Schema
RDFS, OWL, and defining what your graph means
⚑
Ch 15
Graph Embeddings
Translating graph structure into vectors
🧠Part 5 β€” Graph Neural Networks
⚑🎯
Ch 16
Graph Neural Networks
Deep learning on graph-structured data
Ch 17
GNN Architecture Patterns
Depth, pooling, skip connections, and over-smoothing
Ch 18
GNN Applications
Drug discovery, recommendation, fraud detection, traffic
Ch 19
GNN Training at Scale
Sampling, partitioning, and distributed GNN training
πŸ”Part 6 β€” Graph RAG & Retrieval
⚑🎯
Ch 20
Graph RAG
Retrieval-augmented generation over knowledge graphs
Ch 21
Multi-Hop Question Answering
Answering questions that require traversing relations
Ch 22
Graph-Enhanced Agents
Agents that use graphs as memory and world models
βš™οΈPart 7 β€” Graph Analytics at Scale
Ch 23
Graph Analytics at Scale
Pregel, GraphX, and distributed graph processing
Ch 24
Streaming Graphs
When edges arrive continuously and you can't recompute
🎯
Ch 25
Graph Compression
Web graphs, succinct structures, and billion-edge storage
πŸ€–Part 8 β€” Graphs in AI Systems
Ch 26
Graphs in LLM Systems
Where graphs meet transformers
⚑
Ch 27
Graph Transformers
Transformers that operate on graphs instead of sequences
Ch 28
Neuro-Symbolic Graphs
Combining graph logic with neural learning
πŸš€Part 9 β€” Engineering Frontier
Ch 29
Graph Infrastructure Stack
What you need to run graphs in production
Ch 30
Graph Engineering Trade-offs
Latency, consistency, expressiveness, cost β€” you can't have everything
Ch 31
The Graph Engineering Frontier
Where graphs and AI are heading together
🧩Part 10 β€” Compiler & Program Graphs
🎯
Ch 32
Abstract Syntax Trees
Parsing code into a structured graph of meaning
🎯
Ch 33
Control-Flow Graphs
Program execution as a graph of reachable states
🎯
Ch 34
Data Dependence Graphs
Tracing which values flow into which computations
🎯
Ch 35
SSA Form and Def-Use Graphs
Each value has a unique version, and each use points to a definition
🏒Part 11 β€” Organizational Memory Graphs
⚑🎯
Ch 36
OMG: Organizational Memory Graph
The graph of graphs that remembers how an organization thinks, acts, and adapts
πŸ“Part 12 β€” Theory Foundations
🎯
Ch 41
Spectral Graph Theory
Eigenvalues of the Laplacian β€” the algebra behind clustering and GNNs
🎯
Ch 42
Random Graph Models
ErdΕ‘s–RΓ©nyi, BarabΓ‘si–Albert, Watts–Strogatz, stochastic block models
🎯
Ch 43
Graph Homomorphism
Isomorphism, subgraph isomorphism, simulation β€” the unifying abstraction