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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)