Lectures related to Graph Algorithms: Flows and Strongly Connected Components

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

Covers the concept of matroids, focusing on matroid intersection and the properties of subsets of a ground set.

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Explores the Ford-Fulkerson method, max-flow, applications of max-flow, and the disjoint-set data structure.

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.