Directed Networks & Hypergraphs

Related lectures (29)

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

Introduces hypergraphs, generalizing graphs by allowing subsets of nodes to form edges and exploring their applications in various fields.

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

Covers polynomial identity testing using oracles and random point evaluation, with applications in graph theory and algorithmic aspects.

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

Explores irreducible polynomials, focusing on their degree and roots in different fields.

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Covers hypergraphs, complete hypergraphs, link prediction, and scoring methods in network data analysis.

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

Covers the cup product on cohomology, acyclic models, and the universal coefficient theorem.