smile.math.matrix.PageRank Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of smile-base Show documentation
smile-base
There is a newer version: 4.2.0
/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

package smile.math.matrix;

import java.util.Arrays;
import smile.math.MathEx;

/**
 * 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.
 *
 * @author Haifeng Li
 */
public class PageRank {
    private static final org.slf4j.Logger logger = org.slf4j.LoggerFactory.getLogger(PageRank.class);

    /** Private constructor to prevent instance creation. */
    private PageRank() {

    }

    /**
     * Calculates the page rank vector.
     * @param A the matrix supporting matrix vector multiplication operation.
     * @return the page rank vector.
     */
    public static double[] of(IMatrix A) {
        int n = A.nrow();
        double[] v = new double[n];
        Arrays.fill(v, 1.0 / n);
        return of(A, v);
    }

    /**
     * Calculates the page rank vector.
     * @param A the matrix supporting matrix vector multiplication operation.
     * @param v the teleportation vector.
     * @return the page rank vector.
     */
    public static double[] of(IMatrix A, double[] v) {
        return of(A, v, 0.85, 1E-7, 57);
    }

    /**
     * Calculates the page rank vector.
     * @param A the matrix supporting matrix vector multiplication operation.
     * @param v the teleportation vector.
     * @param damping the damper factor.
     * @param tol the desired convergence tolerance.
     * @param maxIter the maximum number of iterations in case that the
     *                algorithm does not converge.
     * @return the page rank vector.
     */
    public static double[] of(IMatrix A, double[] v, double damping, double tol, int maxIter) {
        if (A.nrow() != A.ncol()) {
            throw new IllegalArgumentException("Matrix is not square.");
        }

        if (tol <= 0.0) {
            throw new IllegalArgumentException("Invalid tolerance: " + tol);
        }

        if (maxIter <= 0) {
            throw new IllegalArgumentException("Invalid maximum number of iterations: " + maxIter);
        }

        int n = A.nrow();
        tol = Math.max(tol, MathEx.EPSILON * n);

        double[] z = new double[n];
        double[] p = Arrays.copyOf(v, n);

        for (int iter = 1; iter <= maxIter; iter++) {
            A.mv(p, z);
            double beta = 1.0 - damping * MathEx.norm1(z);

            double delta = 0.0;
            for (int i = 0; i < n; i++) {
                double q = damping * z[i] + beta * v[i];
                delta += Math.abs(q - p[i]);
                p[i] = q;
            }

            if (iter % 10 == 0) {
                logger.info("PageRank residual after {} power iterations: {}", iter, delta);
            }

            if (delta < tol) {
                logger.info("PageRank residual after {} power iterations: {}", iter, delta);
                return p;
            }
        }

        logger.error("PageRank iteration exceeded the maximum number of iterations.");
        return p;
    }
}