Lectures related to Hypergraphs and Link Prediction: Statistical Analysis of Network Data

Covers recent advancements in the dimer model, emphasizing its applications in probability and conformal field theory.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

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

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Covers graphical models for probability distributions and factor graphs representation.