smile.math.matrix

Class PageRank

java.lang.Object

smile.math.matrix.PageRank

public class PageRank
extends java.lang.Object

PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references.

PageRank can be computed either iteratively or algebraically. The iterative method can be viewed as the power iteration method.

Constructor Summary

Constructors

Constructor and Description
PageRank()

Method Summary

Modifier and Type	Method and Description
static double[]	pagerank(Matrix A) Calculate the page rank vector.
static double[]	pagerank(Matrix A, double[] v) Calculate the page rank vector.
static double[]	pagerank(Matrix A, double[] v, double damping, double tol, int maxIter) Calculate the page rank vector.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PageRank

public PageRank()

Method Detail

pagerank

public static double[] pagerank(Matrix A)

Calculate the page rank vector.

Parameters:

A - the matrix supporting matrix vector multiplication operation.

Returns:

the page rank vector.

pagerank

public static double[] pagerank(Matrix A,
                                double[] v)

Calculate the page rank vector.

Parameters:

A - the matrix supporting matrix vector multiplication operation.

v - the teleportation vector.

Returns:

the page rank vector.

pagerank

public static double[] pagerank(Matrix A,
                                double[] v,
                                double damping,
                                double tol,
                                int maxIter)

Calculate the page rank vector.

Parameters:

A - the matrix supporting matrix vector multiplication operation.

v - the teleportation vector.

damping - the damper factor.

tol - the desired convergence tolerance.

maxIter - the maximum number of iterations in case that the algorithm does not converge.

Returns:

the page rank vector.