Percolation: Random Graph Models

Related lectures (32)

Page 3 of 4

Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.

Explores coordination in networked control systems, graph theory, and consensus algorithms.

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

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

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

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

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Explains message passing in graphical models and the matching problem in graph theory.

Explores Cheeger's inequalities for random walks on graphs and their implications.

Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.