Linked from

The 21 pages that link to PageRank, each with the reason it gives.

• Graph theoryRelated: It applies graph structure to estimate the relative importance of web pages.

• EigenvalueRelated: Its rankings arise from an eigenvector of a modified web-link matrix.

• Adjacency matrixRelated: A normalized adjacency matrix defines the transitions used by the random walk.

• Search engineRelated: It contributed a signal of page authority beyond matching query words.

• Sparse matrixRelated: Its computation uses the sparse transition structure of the web graph.

• Search engine optimizationRelated: It illustrates how inbound links became signals of page importance.

• Fixed-point iterationRelated: Power iteration repeatedly applies a matrix map to approximate its defining vector.

• Spectral radiusRelated: Its power iteration relies on spectral properties of a stochastic matrix.

• Degree matrixRelated: Transition matrices for link graphs normalize outgoing links, closely related to degree-based scaling.

• Spectral graph theoryRelated: Its ranking vector is a dominant eigenvector of a modified link matrix.