Graphs and matrices

Related lectures (30)

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Explores the Laplacian matrix and consensus in networked control systems.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

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

Covers graphical models for probability distributions and factor graphs representation.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

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

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

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

Covers the concept of renormalization in Algebraic Quantum Field Theory.