Concept

PageRank

Related lectures (32)

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.

Explores the significance of ranking in networks, emphasizing algorithms like PageRank and HITS for web page ranking.

Explores link-based ranking through PageRank and HITS algorithms, covering practical examples and challenges in web search and ranking methods.

Explores anchor text indexing, PageRank, and link-based ranking using a random walker model.

Explores link-based ranking with a focus on PageRank algorithm and its practical application in web search engines.

Explores recurrent states, invariant distributions, convergence to equilibrium, and PageRank algorithm.

Explores Page Rank algorithm theory, convergence speed, ergodic chains, spectral gap, and chain examples.

Covers the practical computation of PageRank using iterative methods and the challenges of the eigen-vector method.

Explores link-based ranking, document classification, and graph mining techniques.

Covers the Random Walker Model, PageRank algorithm, and community detection in networks.

Explains PageRank and HITS algorithms for web page ranking and authority calculation.

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

Explores graph mining in social networks, covering modularity algorithms and community detection.

Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.

Explores Hertz theory for contact problems and the Tabor measurement method.

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

Explores the significance of anchor text in link-based ranking and its impact on search results.

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