edu.uci.ics.jung.algorithms.importance

Class WeightedNIPaths<V,E>

java.lang.Object

edu.uci.ics.jung.algorithms.util.IterativeProcess

edu.uci.ics.jung.algorithms.importance.AbstractRanker<V,E>

edu.uci.ics.jung.algorithms.importance.WeightedNIPaths<V,E>

All Implemented Interfaces:

IterativeContext

public class WeightedNIPaths<V,E>
extends AbstractRanker<V,E>

This algorithm measures the importance of nodes based upon both the number and length of disjoint paths that lead to a given node from each of the nodes in the root set. Specifically the formula for measuring the importance of a node is given by: I(t|R) = sum_i=1_|P(r,t)|_{alpha^|p_i|} where alpha is the path decay coefficient, p_i is path i and P(r,t) is a set of maximum-sized node-disjoint paths from r to t.

This algorithm uses heuristic breadth-first search to try and find the maximum-sized set of node-disjoint paths between two nodes. As such, it is not guaranteed to give exact answers.

A simple example of usage is:

 WeightedNIPaths ranker = new WeightedNIPaths(someGraph,2.0,6,rootSet);
 ranker.evaluate();
 ranker.printRankings();
 

See Also:

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

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String	WEIGHTED_NIPATHS_KEY

Constructor Summary

Constructors

Constructor and Description
WeightedNIPaths(DirectedGraph<V,E> graph, com.google.common.base.Supplier<V> vertexFactory, com.google.common.base.Supplier<E> edgeFactory, double alpha, int maxDepth, java.util.Set<V> priors) Constructs and initializes the algorithm.

Method Summary

Modifier and Type	Method and Description
java.lang.String	getRankScoreKey() Given a node, returns the corresponding rank score.
void	step() Evaluate the result of the current iteration.

Methods inherited from class edu.uci.ics.jung.algorithms.importance.AbstractRanker

getEdgeRankScore, getEdgeRankScore, getEdgeRankScores, getEdgeRankScores, getEdgeWeights, getRankings, getRankScores, getVertexRankScore, getVertexRankScore, getVertexRankScores, getVertexRankScores, isRankingEdges, isRankingNodes, printRankings, reset, setEdgeWeights, setNormalizeRankings, setRemoveRankScoresOnFinalize

Methods inherited from class edu.uci.ics.jung.algorithms.util.IterativeProcess

done, evaluate, getDesiredPrecision, getIterations, getMaximumIterations, getPrecision, hasConverged, relativePrecision, setDesiredPrecision, setMaximumIterations, setPrecision

Methods inherited from class java.lang.Object

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

Field Detail

WEIGHTED_NIPATHS_KEY

public static final java.lang.String WEIGHTED_NIPATHS_KEY

See Also:

Constant Field Values

Constructor Detail

WeightedNIPaths

public WeightedNIPaths(DirectedGraph<V,E> graph,
                       com.google.common.base.Supplier<V> vertexFactory,
                       com.google.common.base.Supplier<E> edgeFactory,
                       double alpha,
                       int maxDepth,
                       java.util.Set<V> priors)

Constructs and initializes the algorithm.

Parameters:

graph - the graph whose nodes are being measured for their importance

vertexFactory - used to generate instances of V

edgeFactory - used to generate instances of E

alpha - the path decay coefficient (≥1); 2 is recommended

maxDepth - the maximal depth to search out from the root set

priors - the root set (starting vertices)

Method Detail

step

public void step()

Description copied from class: IterativeProcess

Evaluate the result of the current iteration.

Specified by:

step in interface IterativeContext

Specified by:

step in class IterativeProcess

getRankScoreKey

public java.lang.String getRankScoreKey()

Given a node, returns the corresponding rank score. This implementation of getRankScore assumes the decoration representing the rank score is of type MutableDouble.

Specified by:

getRankScoreKey in class AbstractRanker<V,E>

Returns:

the rank score for this node