Expander Graphs: Properties and Eigenvalues

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

Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

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

Explores the construction of Ramanujan graphs using polynomials and addresses challenges with the probabilistic method.

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

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

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.