Class KStepMarkov<V,E>

java.lang.Object

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

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

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

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

All Implemented Interfaces:

VertexScorer<V,Double>, IterativeContext

public class KStepMarkov<V,E>
extends PageRankWithPriors<V,E>

A special case of PageRankWithPriors in which the final scores represent a probability distribution over position assuming a random (Markovian) walk of exactly k steps, based on the initial distribution specified by the priors.

NOTE: The version of KStepMarkov in algorithms.importance (and in JUNG 1.x) is believed to be incorrect: rather than returning a score which represents a probability distribution over position assuming a k-step random walk, it returns a score which represents the sum over all steps of the probability for each step. If you want that behavior, set the 'cumulative' flag as follows before calling evaluate():

    KStepMarkov ksm = new KStepMarkov(...);
    ksm.setCumulative(true);
    ksm.evaluate();

By default, the 'cumulative' flag is set to false. NOTE: THIS CLASS IS NOT YET COMPLETE. USE AT YOUR OWN RISK. (The original behavior is captured by the version still available in algorithms.importance.)

See Also:

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

Field Summary

Fields

Modifier and Type	Field	Description
private boolean	cumulative

Fields inherited from class PageRankWithPriors

disappearing_potential

Fields inherited from class AbstractIterativeScorerWithPriors

alpha, vertex_priors

Fields inherited from class AbstractIterativeScorer

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

Constructor Summary

Constructors

Constructor	Description
KStepMarkov(Hypergraph<V,E> graph, int steps)	Creates an instance based on the specified graph and number of steps to take.
KStepMarkov(Hypergraph<V,E> graph, com.google.common.base.Function<E, ? extends Number> edge_weights, com.google.common.base.Function<V,Double> vertex_priors, int steps)	Creates an instance based on the specified graph, edge weights, vertex priors (initial scores), and number of steps to take.
KStepMarkov(Hypergraph<V,E> graph, com.google.common.base.Function<V,Double> vertex_priors, int steps)	Creates an instance based on the specified graph, vertex priors (initial scores), and number of steps to take.

Method Summary

Modifier and Type	Method	Description
private void	initialize(int steps)
void	setCumulative(boolean cumulative)	Specifies whether this instance should assign a score to each vertex based on the sum over all steps of the probability for each step.
double	update(V v)	Updates the value for this vertex.

Methods inherited from class PageRankWithPriors

afterStep, collectDisappearingPotential

Methods inherited from class AbstractIterativeScorerWithPriors

getAlpha, getVertexPrior, getVertexPriors, initialize

Methods inherited from class 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 Object

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

Methods inherited from interface VertexScorer

getVertexScore

Field Details

cumulative

private boolean cumulative

Constructor Details

KStepMarkov

public KStepMarkov(Hypergraph<V,E> graph,
 com.google.common.base.Function<E, ? extends Number> edge_weights,
 com.google.common.base.Function<V,Double> vertex_priors,
 int steps)

Creates an instance based on the specified graph, edge weights, vertex priors (initial scores), and number of steps to take.

Parameters:

graph - the input graph

edge_weights - the edge weights (transition probabilities)

vertex_priors - the initial probability distribution (score assignment)

steps - the number of times that step() will be called by evaluate

KStepMarkov

public KStepMarkov(Hypergraph<V,E> graph,
 com.google.common.base.Function<V,Double> vertex_priors,
 int steps)

Creates an instance based on the specified graph, vertex priors (initial scores), and number of steps to take. The edge weights (transition probabilities) are set to default values (a uniform distribution over all outgoing edges).

Parameters:

graph - the input graph

vertex_priors - the initial probability distribution (score assignment)

steps - the number of times that step() will be called by evaluate

KStepMarkov

public KStepMarkov(Hypergraph<V,E> graph,
 int steps)

Creates an instance based on the specified graph and number of steps to take. The edge weights (transition probabilities) and vertex initial scores (prior probabilities) are set to default values (a uniform distribution over all outgoing edges, and a uniform distribution over all vertices, respectively).

Parameters:

graph - the input graph

steps - the number of times that step() will be called by evaluate

Method Details

initialize

private void initialize(int steps)

setCumulative

public void setCumulative(boolean cumulative)

Specifies whether this instance should assign a score to each vertex based on the sum over all steps of the probability for each step. See the class-level documentation for details.

Parameters:

cumulative - true if this instance should assign a cumulative score to each vertex

update

public double update(V v)

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

Overrides:

update in class PageRankWithPriors<V,E>

Parameters:

v - the vertex whose value is to be updated

Returns:

the updated value