For ranking vertices of a graph like pagerank and textrank.

graphranker.py

import numpy as np
from copy import deepcopy

class GraphRanker(object):
    def __init__(self, d=0.85, tol=1e-6, max_iters=200):
        self.d = d
        self.tol = tol
        self.max_iters = max_iters

    def fit(self, A):
        """
        A: adjacency matrix
        """
        n = A.shape[0]
        A = A / A.sum(axis=1)
        A[np.isnan(A)] = 0.0
        f = np.ones((n, 1))
        for _ in xrange(self.max_iters):
            f_prev = f.copy()
            f = (1-self.d) + self.d*A.T.dot(f)
            diff = np.max(np.abs(f-f_prev))
            if diff < self.tol:
                print "converged", _
                break
        self.scores = np.asarray(f).ravel()
        return self