Graph Coloring: Basics and Applications

Related lectures (29)

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Distinguishes between subgraphs and induced subgraphs in graph theory, illustrating the construction of minimal spanning trees.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

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

Explores graph coloring, directed cycles, LLL algorithm applications, and element dependencies in graphs.

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

Covers graphical models for probability distributions and factor graphs representation.

Explores Belief Propagation for graph coloring and its convergence properties.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

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