Graph Theory

From sets and proofs to spectral methods and research frontiers

A rigorous, ever-expanding journey through graph theory — mathematical foundations, traversal, shortest paths, trees, flows, coloring, planarity, structural and extremal theory, probabilistic and spectral methods, and modern graph machine learning. Every part builds intuition first, then formalism, then algorithms.

28Parts
32Algorithm Deep Dives
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28-Part Curriculum — Complete!

All Articles in This Series

A complete path from discrete-math prerequisites through algorithmic graph theory to structural, extremal, probabilistic, machine-learning, scientific, and research-frontier topics. All 28 parts are now published!

32 Algorithm Deep Dives

Algorithm Deep Dives

Standalone reference guides for individual graph algorithms — full derivation, pseudocode, Python/C++/Java implementations, complexity analysis, and worked examples. Read independently of the main parts.

All 28 main parts are published, and the algorithm deep dive library keeps growing — 32 and counting!