NAME
"Graph::Centrality::Pagerank" - Computes pagerank of all nodes in a
graph.
SYNOPSIS
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2],[3,4]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges);
# dumps:
# {
# 1 => "0.175438596989046",
# 2 => "0.324561403010954",
# 3 => "0.175438596989046",
# 4 => "0.324561403010954",
# }
DESCRIPTION
"Graph::Centrality::Pagerank" computes the pagerank of the all nodes in
a graph. The input can be a list of edges or a Graph.
"Graph::Centrality::Pagerank" is written entirely in Perl and is not
meant for use in high performance applications.
CONSTRUCTOR
"new"
The method "new" creates an instance of the
"Graph::Centrality::Pagerank" class with the following parameters:
"dampeningFactor"
dampeningFactor => 0.85
"dampeningFactor" is the dampening factor used when computing
pagerank. It must be a value ranging from zero to one; the default
is 0.85. Note the incidence matrix generated from the graph is
multiplied (scaled) by "dampeningFactor", *not* by "1 -
dampeningFactor".
"maxRelError"
maxRelError => sqrt (machine-epsilon)
"maxRelError" is the maximum *average* relative error that is
permitted between successive pagerank vectors before the iterative
process to compute the vectors should be stopped. The default is the
square root of the systems machine epsilon. Usually, most pagerank
values computed will have "-log10(maxRelError)" digits of accuracy.
"maxRelError" must be positive and less than or equal to 0.01.
"minIterations"
minIterations => 0
"minIterations" is the minimum number of iterations that will be
computed before the pagerank iterations are stopped, even if
"maxRelError" is achieved. The default is zero.
"maxIterations"
maxIterations => int (2 * ((maxRelError / ln (dampeningFactor) + 1))
"maxIterations" is the maximum number of iterations that can be
performed to approximate the pagerank vector even if "maxRelError"
is not achieved. The default is "2 * ((maxRelError / ln
(dampeningFactor) + 1)". If "dampeningFactor" is zero, then
"maxIterations" is one. If "dampeningFactor" is one, then
"maxIterations" is equal to the total nodes in the graph.
"linkSinkNodes"
linkSinkNodes => 1
In a directed graph sink nodes are the nodes with no edges emanating
out from them. In the pagerank algorithm these nodes are
automatically linked to all other nodes in the graph. To prevent
this set "linkSinkNodes" to zero; the default is one.
"directed"
directed => 1
If "directed" is true, the pagerank computations are done with the
graph edges being directed. If "directed" is false, the pageranks
are computed treating the graph as undirected; the default value of
"directed" is one.
"useEdgeWeights"
useEdgeWeights => 0
If "useEdgeWeights" is true, then any weight associated with an edge
is used in the computation of pagerank. The default weight for any
edge without an assigned weight is one. The default value of
"useEdgeWeights" is zero, which forces all edge weights to be one.
METHOD
"getPagerankOfNodes"
The method "getPagerankOfNodes" computes the pagerank of each node in
the graph. The graph can be defined using the "graph" parameter or by
supplying a list of edges. All the parameters used by the constructor
"new" can also be set here and they will override the values used with
"new". "getPagerankOfNodes" returns a reference to a hash where the keys
are the graph nodes and the values are the pageranks of the node.
"graph"
graph => Graph
"graph" must be a Graph object. If the "directed" parameter was not
set with the constructor "new" or with this method, then "directed"
is set to the value of Graph->is_directed().
"listOfEdges"
listOfEdges => [['a',10],[10,11]]
"listOfEdges" must point to a list of edges, where an edge is a pair
of strings of the form "[from-node, to-node]" or a triple of the
form "[from-node, to-node, numeric-edge-weight]". Note that "graph"
and "listOfEdges" can both be defined, in which case the union of
their list of edges is used to compute the pageranks of the nodes.
EXAMPLES
An example of a graph with two components:
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2],[3,4]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges);
# dumps:
# {
# 1 => "0.175438596989046",
# 2 => "0.324561403010954",
# 3 => "0.175438596989046",
# 4 => "0.324561403010954",
# }
In this case the edges are placed in a Graph:
use Graph;
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $listOfEdges = [[1,2],[3,4]];
my $graph = Graph->new (edges => $listOfEdges);
my $ranker = Graph::Centrality::Pagerank->new();
dump $ranker->getPagerankOfNodes (graph => $graph);
# dumps:
# {
# 1 => "0.175438596989046",
# 2 => "0.324561403010954",
# 3 => "0.175438596989046",
# 4 => "0.324561403010954",
# }
Below is the first example in the paper *How Google Finds Your Needle in
the Web's Haystack* by David Austin.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2],[1,3],[2,4],[3,2],[3,5],[4,2],[4,5],[4,6],[5,6],
[5,7],[5,8],[6,8],[7,5],[7,1],[7,8],[8,6],[8,7]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
dampeningFactor => 1);
# dumps:
# {
# 1 => "0.0599999994835539",
# 2 => "0.0675000002254998",
# 3 => "0.0300000002967361",
# 4 => "0.0674999997408677",
# 5 => "0.0974999994123176",
# 6 => "0.202500001447512",
# 7 => "0.180000001348251",
# 8 => "0.294999998045262",
# }
Below is the second example in the paper. Notice "linkSinkNodes" is set
to zero.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
dampeningFactor => 1, linkSinkNodes => 0);
# dumps:
# { 1 => 0, 2 => 0 }
Below is the third example in the paper. Notice in this case
"linkSinkNodes" is set to one, the default value.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
dampeningFactor => 1, linkSinkNodes => 1);
# dumps:
# { 1 => "0.33333333209157", 2 => "0.66666666790843" }
Below is the forth example in the paper. The result is different from
the paper since the starting vector for Graph::Centrality::Pagerank is
{ 1 => "0.2", 2 => "0.2", 3 => "0.2", 4 => "0.2", 5 => "0.2" }
while the starting vector in the paper is
{ 1 => 1, 2 => 0, 3 => 0, 4 => 0, 5 => 0 }.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,2],[2,3],[3,4],[4,5],[5,1]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
dampeningFactor => 1, linkSinkNodes => 0);
# dumps:
# { 1 => "0.2", 2 => "0.2", 3 => "0.2", 4 => "0.2", 5 => "0.2" }
Below is the fifth example in the paper.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[1,3],[1,2],[2,4],[3,2],[3,5],[4,2],[4,5],[4,6],[5,6],
[5,7],[5,8],[6,8],[7,5],[7,8],[8,6],[8,7]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
dampeningFactor => 1, linkSinkNodes => 0);
# dumps:
# {
# 1 => 0,
# 2 => "2.39601089109228e-54",
# 3 => 0,
# 4 => "5.47659632249665e-54",
# 5 => "0.119999999997811",
# 6 => "0.240000000003975",
# 7 => "0.240000000003975",
# 8 => "0.399999999994238",
# }
An example of the effect of including edge weights:
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my $listOfEdges = [[2,1],[2,3]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges);
$listOfEdges = [[2,1,2],[2,3,1]];
dump $ranker->getPagerankOfNodes (listOfEdges => $listOfEdges,
useEdgeWeights => 1);
# dumps:
# all edges have weight 1.
# {
# 1 => "0.370129870353883",
# 2 => "0.259740259292235",
# 3 => "0.370129870353883",
# }
# edge [2, 1] has twice the weight of edge [2,3].
# {
# 1 => "0.406926407374432",
# 2 => "0.259740259292235",
# 3 => "0.333333333333333",
# }
}
A example of the modules speed, or lack of.
use Graph::Centrality::Pagerank;
use Data::Dump qw(dump);
my $ranker = Graph::Centrality::Pagerank->new();
my @listOfEdges;
for (my $i = 0; $i < 1000000; $i++)
{ push @listOfEdges, [int rand 10000, int rand 10000]; }
my $startTime = time;
my $pageranks = $ranker->getPagerankOfNodes (listOfEdges => \@listOfEdges);
print time()-$startTime . "\n";
# prints:
# a non-negative integer after a long time.
INSTALLATION
To install the module run the following commands:
perl Makefile.PL
make
make test
make install
If you are on a windows box you should use 'nmake' rather than 'make'.
AUTHOR
Jeff Kubina
COPYRIGHT
Copyright (c) 2009 Jeff Kubina. All rights reserved. This program is
free software; you can redistribute it and/or modify it under the same
terms as Perl itself.
The full text of the license can be found in the LICENSE file included
with this module.
SEE ALSO
A good tutorial on the pagerank algorithm is the article *How Google
Finds Your Needle in the Web's Haystack* by David Austin.
Graph