/usr/lib/python3/dist-packages/pygraph/algorithms/pagerank.py is in python3-pygraph 1.8.2-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | # Copyright (c) 2010 Pedro Matiello <pmatiello@gmail.com>
# Juarez Bochi <jbochi@gmail.com>
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without
# restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following
# conditions:
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
"""
PageRank algoritm
@sort: pagerank
"""
def pagerank(graph, damping_factor=0.85, max_iterations=100, min_delta=0.00001):
"""
Compute and return the PageRank in an directed graph.
@type graph: digraph
@param graph: Digraph.
@type damping_factor: number
@param damping_factor: PageRank dumping factor.
@type max_iterations: number
@param max_iterations: Maximum number of iterations.
@type min_delta: number
@param min_delta: Smallest variation required to have a new iteration.
@rtype: Dict
@return: Dict containing all the nodes PageRank.
"""
nodes = graph.nodes()
graph_size = len(nodes)
if graph_size == 0:
return {}
min_value = (1.0-damping_factor)/graph_size #value for nodes without inbound links
# itialize the page rank dict with 1/N for all nodes
pagerank = dict.fromkeys(nodes, 1.0/graph_size)
for i in range(max_iterations):
diff = 0 #total difference compared to last iteraction
# computes each node PageRank based on inbound links
for node in nodes:
rank = min_value
for referring_page in graph.incidents(node):
rank += damping_factor * pagerank[referring_page] / len(graph.neighbors(referring_page))
diff += abs(pagerank[node] - rank)
pagerank[node] = rank
#stop if PageRank has converged
if diff < min_delta:
break
return pagerank
|