If you want to actually understand graphs / networks, here’s my recommendation:
Foundations:
> Barabási – Network Science
> Easley & Kleinberg – Networks, Crowds, and Markets
Math backbone:
> MIT – Math for CS
> Diestel – Graph Theory
Algorithms layer:
> Kleinberg & Tardos – Algorithm Design
> MIT graph lectures (for intuition fast)
Deeper theory:
> Spielman – Spectral Graph Theory
Systems / markets angle:
> Roughgarden – Algorithmic Game Theory
Modern AI angle:
> Stanford CS224W (Graph ML)
> PyTorch Geometric (when you actually build)
Hands-on (this is where it clicks):
> NetworkX (start here)
> Gephi (visual intuition)
> SNAP (real datasets)
> graph-tool (when you care about speed)
Ahmad@TheAhmadOsmanSpending time learning graphs and networking theory is one of the highest-ROI investments you can make. It quietly compounds across distributed systems, AI, infrastructure, markets, and even social dynamics. Boring on day one. Unfair advantage by year two.