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104 lines
3.0 KiB
104 lines
3.0 KiB
2 years ago
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/*!
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* \file
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*
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* \brief Various routines that perform random-walk based operations
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on graphs stored as gk_csr_t matrices.
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*
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* \author George Karypis
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* \version\verbatim $Id: rw.c 11078 2011-11-12 00:20:44Z karypis $ \endverbatim
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*/
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#include <GKlib.h>
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/*************************************************************************/
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/*! Computes the (personalized) page-rank of the vertices in a graph.
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\param mat is the matrix storing the graph.
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\param lamda is the restart probability.
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\param eps is the error tolerance for convergance.
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\param max_niter is the maximum number of allowed iterations.
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\param pr on entry stores the restart distribution of the vertices.
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This allows for the computation of personalized page-rank scores
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by appropriately setting that parameter.
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On return, pr stores the computed page ranks.
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\returns the number of iterations that were performed.
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*/
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/**************************************************************************/
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int gk_rw_PageRank(gk_csr_t *mat, float lamda, float eps, int max_niter, float *pr)
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{
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ssize_t i, j, k, iter, nrows;
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double *rscale, *prold, *prnew, *prtmp;
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double fromsinks, error;
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ssize_t *rowptr;
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int *rowind;
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float *rowval;
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nrows = mat->nrows;
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rowptr = mat->rowptr;
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rowind = mat->rowind;
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rowval = mat->rowval;
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prold = gk_dsmalloc(nrows, 0, "gk_rw_PageRank: prnew");
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prnew = gk_dsmalloc(nrows, 0, "gk_rw_PageRank: prold");
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rscale = gk_dsmalloc(nrows, 0, "gk_rw_PageRank: rscale");
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/* compute the scaling factors to get adjacency weights into transition
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probabilities */
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for (i=0; i<nrows; i++) {
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for (j=rowptr[i]; j<rowptr[i+1]; j++)
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rscale[i] += rowval[j];
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if (rscale[i] > 0)
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rscale[i] = 1.0/rscale[i];
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}
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/* the restart distribution is the initial pr scores */
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for (i=0; i<nrows; i++)
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prnew[i] = pr[i];
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/* get into the PR iteration */
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for (iter=0; iter<max_niter; iter++) {
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gk_SWAP(prnew, prold, prtmp);
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gk_dset(nrows, 0.0, prnew);
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/* determine the total current PR score of the sinks so that you
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can distribute them to all nodes according to the restart
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distribution. */
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for (fromsinks=0.0, i=0; i<nrows; i++) {
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if (rscale[i] == 0)
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fromsinks += prold[i];
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}
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/* push random-walk scores to the outlinks */
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for (i=0; i<nrows; i++) {
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for (j=rowptr[i]; j<rowptr[i+1]; j++)
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prnew[rowind[j]] += prold[i]*rscale[i]*rowval[j];
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}
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/* apply the restart conditions */
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for (i=0; i<nrows; i++) {
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prnew[i] = lamda*(fromsinks*pr[i]+prnew[i]) + (1.0-lamda)*pr[i];
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}
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/* compute the error */
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for (error=0.0, i=0; i<nrows; i++)
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error = (fabs(prnew[i]-prold[i]) > error ? fabs(prnew[i]-prold[i]) : error);
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//printf("nrm1: %le maxfabserr: %le\n", gk_dsum(nrows, prnew, 1), error);
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if (error < eps)
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break;
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}
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/* store the computed pr scores into pr for output */
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for (i=0; i<nrows; i++)
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pr[i] = prnew[i];
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gk_free((void **)&prnew, &prold, &rscale, LTERM);
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return (int)(iter+1);
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}
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