Isogenic Graphs: Spectral Analysis and Mathematical Applications

Related lectures (30)

Page 1 of 3

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

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.

Explores the application of matrices and eigendecompositions in networks.

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

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

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

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Covers Cheeger's inequalities and the combinatorial properties of graphs.