edu.uci.ics.jung.algorithms.scoring

Class PageRankWithPriors<V,E>

java.lang.Object

edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorer<V,E,S>

edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorerWithPriors<V,E,Double>

edu.uci.ics.jung.algorithms.scoring.PageRankWithPriors<V,E>

All Implemented Interfaces:

VertexScorer<V,Double>, IterativeContext

Direct Known Subclasses:

KStepMarkov, PageRank

public class PageRankWithPriors<V,E>
extends AbstractIterativeScorerWithPriors<V,E,Double>

A generalization of PageRank that permits non-uniformly-distributed random jumps. The 'vertex_priors' (that is, prior probabilities for each vertex) may be thought of as the fraction of the total 'potential' that is assigned to that vertex at each step out of the portion that is assigned according to random jumps (this portion is specified by 'alpha').

See Also:

"Algorithms for Estimating Relative Importance in Graphs by Scott White and Padhraic Smyth, 2003", PageRank

Field Summary

Fields

Modifier and Type	Field and Description
protected double	disappearing_potential Maintains the amount of potential associated with vertices with no out-edges.

Fields inherited from class edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorerWithPriors

alpha, vertex_priors

Fields inherited from class edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorer

edge_weights, graph, hyperedges_are_self_loops, max_delta, max_iterations, output_reversed, tolerance, total_iterations

Constructor Summary

Constructors

Constructor and Description
PageRankWithPriors(Hypergraph<V,E> graph, com.google.common.base.Function<E,? extends Number> edge_weights, com.google.common.base.Function<V,Double> vertex_priors, double alpha) Creates an instance with the specified graph, edge weights, vertex priors, and 'random jump' probability (alpha).
PageRankWithPriors(Hypergraph<V,E> graph, com.google.common.base.Function<V,Double> vertex_priors, double alpha) Creates an instance with the specified graph, vertex priors, and 'random jump' probability (alpha).

Method Summary

Modifier and Type	Method and Description
protected void	afterStep() Cleans up after each step.
protected void	collectDisappearingPotential(V v) Collects the "disappearing potential" associated with vertices that have no outgoing edges.
double	update(V v) Updates the value for this vertex.

Methods inherited from class edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorerWithPriors

getAlpha, getVertexPrior, getVertexPriors, initialize

Methods inherited from class edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorer

acceptDisconnectedGraph, done, evaluate, getAdjustedIncidentCount, getCurrentValue, getEdgeWeight, getEdgeWeights, getIterations, getMaxIterations, getOutputValue, getTolerance, getVertexScore, isDisconnectedGraphOK, setCurrentValue, setEdgeWeights, setHyperedgesAreSelfLoops, setMaxIterations, setOutputValue, setTolerance, step, swapOutputForCurrent, updateMaxDelta

Methods inherited from class java.lang.Object

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

Methods inherited from interface edu.uci.ics.jung.algorithms.scoring.VertexScorer

getVertexScore

Field Detail

disappearing_potential

protected double disappearing_potential

Maintains the amount of potential associated with vertices with no out-edges.

Constructor Detail

PageRankWithPriors

public PageRankWithPriors(Hypergraph<V,E> graph,
                          com.google.common.base.Function<E,? extends Number> edge_weights,
                          com.google.common.base.Function<V,Double> vertex_priors,
                          double alpha)

Creates an instance with the specified graph, edge weights, vertex priors, and 'random jump' probability (alpha).

Parameters:

graph - the input graph

edge_weights - the edge weights, denoting transition probabilities from source to destination

vertex_priors - the prior probabilities for each vertex

alpha - the probability of executing a 'random jump' at each step

PageRankWithPriors

public PageRankWithPriors(Hypergraph<V,E> graph,
                          com.google.common.base.Function<V,Double> vertex_priors,
                          double alpha)

Creates an instance with the specified graph, vertex priors, and 'random jump' probability (alpha). The outgoing edge weights for each vertex will be equal and sum to 1.

Parameters:

graph - the input graph

vertex_priors - the prior probabilities for each vertex

alpha - the probability of executing a 'random jump' at each step

Method Detail

update

public double update(V v)

Updates the value for this vertex. Called by step().

Specified by:

update in class AbstractIterativeScorer<V,E,Double>

Parameters:

v - the vertex whose value is to be updated

Returns:

the updated value

afterStep

protected void afterStep()

Cleans up after each step. In this case that involves allocating the disappearing potential (thus maintaining normalization of the scores) according to the vertex probability priors, and then calling super.afterStep.

Overrides:

afterStep in class AbstractIterativeScorer<V,E,Double>

collectDisappearingPotential

protected void collectDisappearingPotential(V v)

Collects the "disappearing potential" associated with vertices that have no outgoing edges. Vertices that have no outgoing edges do not directly contribute to the scores of other vertices. These values are collected at each step and then distributed across all vertices as a part of the normalization process.

Overrides:

collectDisappearingPotential in class AbstractIterativeScorer<V,E,Double>

Parameters:

v - the vertex whose potential is being collected