nz.ac.waikato.mcennis.rat.graph.algorithm.prestige
Class AddPageRankPrestige

java.lang.Object
  nz.ac.waikato.mcennis.rat.graph.model.ModelShell
      nz.ac.waikato.mcennis.rat.graph.algorithm.prestige.AddPageRankPrestige

All Implemented Interfaces:

java.io.Serializable, Component, Algorithm, Model

public class AddPageRankPrestige
extends ModelShell
implements Algorithm

Calcuates the PageRank of an actor using the PageRank algorithm as defined in Langeville and Meyer

Langeville, A., and C. Meyer. 2003. "Deeper inside PageRank". Internet Mathematics 1(3):335--80.

See Also:

Serialized Form

Field Summary

Fields inherited from class nz.ac.waikato.mcennis.rat.graph.model.ModelShell

listener

Constructor Summary

AddPageRankPrestige()
Creates a new instance of AddPageRankPrestige

Method Summary

void	execute(Graph g) Implements the PageRank algorithm in a naive fashion - directly calculating the eigenvector matrix and taking the largest eigenvector (using the Colt scientific computing toolkit.)
InputDescriptor[]	getInputType() The input type describes all the different kinds of graph objects that are utilized (and hence required) by this object.
OutputDescriptor[]	getOutputType() The output type describes all the different kinds of graph objects that are created during the execution of this algorithm.
Parameter[]	getParameter() List of all parameters this component accepts.
Parameter	getParameter(java.lang.String param) Returns the specific parameter identified by its key-name.
SettableParameter[]	getSettableParameter() Returns settable (i.e.
SettableParameter	getSettableParameter(java.lang.String param) Return the settable parameter namede by this key-name.
void	init(java.util.Properties map) Parameters for intializing this algorithm 'name' - name of this instance of this algorithm.

Methods inherited from class nz.ac.waikato.mcennis.rat.graph.model.ModelShell

addListener, fireChange

Methods inherited from class java.lang.Object

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

Methods inherited from interface nz.ac.waikato.mcennis.rat.graph.model.Model

addListener

Constructor Detail

AddPageRankPrestige

public AddPageRankPrestige()

Creates a new instance of AddPageRankPrestige

Method Detail

getInputType

public InputDescriptor[] getInputType()

Description copied from interface: Component

The input type describes all the different kinds of graph objects that are utilized (and hence required) by this object. This result is only guaranteed to be fixed if structural parameters are not modified. This is an empty array if there is no input.

Specified by:

getInputType in interface Component

Returns:

InputDescriptor array for this component

See Also:

InputDescriptor

getOutputType

public OutputDescriptor[] getOutputType()

Description copied from interface: Component

The output type describes all the different kinds of graph objects that are created during the execution of this algorithm. The result is only guaranteed to be fixed if structural parameters are not modified. This is an empty array if there is no output.

Specified by:

getOutputType in interface Component

Returns:

OutputDescriptor array for this component

See Also:

OutputDescriptor

getParameter

public Parameter[] getParameter()

Description copied from interface: Component

List of all parameters this component accepts. Each parameter also has a distinct key-name used when initializing the object using the init method. If there are no parameters, null is returned.

Specified by:

getParameter in interface Component

Returns:

read-only array of Parameters

getParameter

public Parameter getParameter(java.lang.String param)

Description copied from interface: Component

Returns the specific parameter identified by its key-name. If no parameter is found with this key-name, null is returned.

Specified by:

getParameter in interface Component

Parameters:

param - key-name of the parameter

Returns:

named parameter

execute

public void execute(Graph g)

Implements the PageRank algorithm in a naive fashion - directly calculating the eigenvector matrix and taking the largest eigenvector (using the Colt scientific computing toolkit.)

Specified by:

execute in interface Algorithm

Parameters:

g - graph to be modified

getSettableParameter

public SettableParameter[] getSettableParameter()

Description copied from interface: Component

Returns settable (i.e. editable while running) parameters. If none exist, null is returned.

Specified by:

getSettableParameter in interface Component

Returns:

array of settable parameters

getSettableParameter

public SettableParameter getSettableParameter(java.lang.String param)

Description copied from interface: Component

Return the settable parameter namede by this key-name. If this parameter is not found or is not settable, null is returned.

Specified by:

getSettableParameter in interface Component

Parameters:

param - key-name of the parameter

Returns:

named settable parameter

init

public void init(java.util.Properties map)

Parameters for intializing this algorithm

1. 'name' - name of this instance of this algorithm. Default is 'Page Rank Centrality'.
2. 'relation' - type (relation) of link to use to create a link matrix. Default is 'Knows'.
3. 'actorSourceType - type (mode) of actor to define Page Rank for. Default is 'User'.
4. 'propertyName' - name for the property to be created. Deafult is 'Knows PageRank Centrality'.
5. 'teleportationFactor' - Percent of links for a given node will link to a 'master node' equally connected to every other node. Deafult is '0.15'.

Input 1 - Link
Output 1 - Actor Property

Specified by:

init in interface Component

Parameters:

map - map of the given properties naming parameters and their values in a string