PageRank & HITS Algorithms

Related lectures (29)

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Explores the application of matrices and eigendecompositions in networks.

Explores eigenvalues and eigenvectors in matrices through examples and calculations.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Introduces the basics of finite element modeling for dynamics and discusses the Newmark method for time integration.

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.

Explores eigenvalues and eigenvectors in matrix transformations, essential for understanding mathematical and real-world systems.

Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.