Created
October 17, 2012 03:09
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pagerank算法
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# 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 |
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