Dense Graphs: From Theory to Applications

Related lectures (29)

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

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Covers graphical models for probability distributions and factor graphs representation.

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Explores the Cavity Method in Mean Field Theory, analyzing spins in an external field within a graph.

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

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

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.