Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Glossary of graph theory

Related lectures (32)

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

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

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Explores graph theory, stochastic matrices, consensus algorithms, and spectral properties in networked control systems.

Introduces unweighted bipartite matching and its solution using linear programming and the simplex method.

Introduces the probabilistic method to prove existence results in graph theory.

Explores the concept of minimizing guardians in graphs through strategic edge selection.

Explores coordination in networked control systems, graph theory, and consensus algorithms.

Covers the definition of flow in a network and flow calculation.

Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.

Covers the calculation of paths in a graph, focusing on amplitude-weighted paths.

Explores Eulerian circuits through the Königsberg bridges problem, leading to the development of graph theory and topology.

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

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

Explores Belief Propagation for graph coloring and its convergence properties.

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

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

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