Lectures related to Graphs and Networks: Basics and Applications

Explores network flows, simplex method, linear programming, tree solutions, and dual solutions in optimization problems.

Covers graphical models for probability distributions and factor graphs representation.

Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Covers Minimum Spanning Trees, Kruskal's Algorithm, and Shortest Paths in directed graphs.

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.