Graph Coloring: Theory and Applications

Related lectures (32)

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Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

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

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Covers the fundamentals of handling networks and centrality measures in graph theory.

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.