Package it.unimi.dsi.law.big.rank

Class PageRank

java.lang.Object

it.unimi.dsi.law.big.rank.SpectralRanking

it.unimi.dsi.law.big.rank.PageRank

Direct Known Subclasses:

PageRankParallelGaussSeidel

public abstract class PageRank
extends SpectralRanking

A big version of PageRank.

See Also:

PageRank, SpectralRanking

Nested Class Summary

Nested classes/interfaces inherited from class it.unimi.dsi.law.big.rank.SpectralRanking

SpectralRanking.IterationNumberStoppingCriterion, SpectralRanking.NormStoppingCriterion, SpectralRanking.StoppingCriterion

Field Summary

Fields

Modifier and Type	Field	Description
double	alpha	The damping factor.
LongArrayBitVector	buckets	If not null, the set of buckets of SpectralRanking.graph.
DoubleBigList	danglingNodeDistribution	The vector used used to patch null rows of the adjacency matrix (u in the general formula).
static double	DEFAULT_ALPHA	The default damping factor.
DoubleBigList	preference	The preference vector to be used (or null if the uniform preference vector should be used).
boolean	stronglyPreferential	Decides whether we use the strongly or weakly (the default) preferential algorithm.

Fields inherited from class it.unimi.dsi.law.big.rank.SpectralRanking

DEFAULT_MAX_ITER, DEFAULT_NORM, DEFAULT_THRESHOLD, graph, iteration, logger, n, rank, STOCHASTIC_TOLERANCE

Constructor Summary

Constructors

Constructor	Description
PageRank(ImmutableGraph g, Logger logger)	Creates a new instance.

Method Summary

Modifier and Type	Method	Description
Properties	buildProperties(String graphBasename, String preferenceFilename, String danglingFilename)	Returns a Properties object that contains all the parameters used by the computation.
void	init()	Basic initialization: we log the damping factor, check that the preference vector is correctly sized and stochastic, fill SpectralRanking.rank with the preference vector and set the dangling-node distribution depending on the value of stronglyPreferential.

Methods inherited from class it.unimi.dsi.law.big.rank.SpectralRanking

and, approximateNormVector, buildProperties, clear, isStochastic, normDelta, or, step, stepUntil

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

DEFAULT_ALPHA

public static final double DEFAULT_ALPHA

The default damping factor.

See Also:

Constant Field Values

alpha

public double alpha

The damping factor. In the random surfer interpretation, this is the probability that the surfer will follow a link in the current page.

preference

public DoubleBigList preference

The preference vector to be used (or null if the uniform preference vector should be used).

danglingNodeDistribution

public DoubleBigList danglingNodeDistribution

The vector used used to patch null rows of the adjacency matrix (u in the general formula). It coincides with the preference vector if stronglyPreferential is true. If null, the uniform distribution will be used.

buckets

public LongArrayBitVector buckets

If not null, the set of buckets of SpectralRanking.graph.

stronglyPreferential

public boolean stronglyPreferential

Decides whether we use the strongly or weakly (the default) preferential algorithm.

Constructor Details

PageRank

public PageRank(ImmutableGraph g,
Logger logger)

Creates a new instance.

Parameters:

g - the graph.

logger - a logger.

Method Details

buildProperties

public Properties buildProperties(String graphBasename,
String preferenceFilename,
String danglingFilename)

Returns a Properties object that contains all the parameters used by the computation.

Parameters:

graphBasename - the basename of the graph.

preferenceFilename - the filename of preference vector. It can be null.

danglingFilename - the filename of dangling-node distribution. It can be null.

Returns:

a properties object that represent all the parameters used to calculate the rank.

init

public void init()
          throws IOException

Basic initialization: we log the damping factor, check that the preference vector is correctly sized and stochastic, fill SpectralRanking.rank with the preference vector and set the dangling-node distribution depending on the value of stronglyPreferential.

Overrides:

init in class SpectralRanking

Throws:

IOException