Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Covers conditional statements and graph theory, including proof techniques and concepts related to graphs, paths, connectivity, and gossip protocols.
Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
Covers the definition of flow in a network and flow calculation.
