Graph Theory for Machine Learning

Graphs are the data structure of relationships. If your data has connections, you need graph theory.

Build 1.5 hr Python

Prerequisites: Phase 1, Lessons 01-03 (linear algebra, matrices)

#Learning Objectives

Build a graph class with adjacency matrix/list representations and implement BFS and DFS traversals
Compute the graph Laplacian and use its eigenvalues to detect connected components and cluster nodes
Implement one round of GNN-style message passing as a normalized adjacency matrix multiplication
Apply spectral clustering to partition a graph using the Fiedler vector

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.