org.graphstream.algorithm.community
Class Leung

java.lang.Object
  org.graphstream.algorithm.community.DecentralizedCommunityAlgorithm
      org.graphstream.algorithm.community.EpidemicCommunityAlgorithm
          org.graphstream.algorithm.community.Leung

All Implemented Interfaces:

Algorithm, DynamicAlgorithm, org.graphstream.stream.AttributeSink, org.graphstream.stream.ElementSink, org.graphstream.stream.Sink

public class Leung
extends EpidemicCommunityAlgorithm

This class implements an improved community detection algorithm based on the epidemic label propagation paradigm the was presented by Leung et al.

Author:

Guillaume-Jean Herbiet

Scientific Reference :

I. X. Y. Leung, P. Hui, P. Lio`, and J. Crowcroft, “Towards Real- Time Community Detection in Large Networks,” Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), vol. 79, no. 6, pp. 066 107+, 2009.

Constructor Summary

Leung()

Leung(org.graphstream.graph.Graph graph)

Leung(org.graphstream.graph.Graph graph, double m, double delta)
Create a new Leung algorithm instance, attached to the specified graph, using the default markers for the node community and edge weight attributes.

Leung(org.graphstream.graph.Graph graph, String marker)

Leung(org.graphstream.graph.Graph graph, String marker, double m, double delta)
Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the default marker to retrieve the weight attribute of graph edges.

Leung(org.graphstream.graph.Graph graph, String marker, String weightMarker)
Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the specified weightMarker to retrieve the weight attribute of graph edges.

Leung(org.graphstream.graph.Graph graph, String marker, String weightMarker, double m, double delta)
Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the specified weightMarker to retrieve the weight attribute of graph edges.

Method Summary

void	computeNode(org.graphstream.graph.Node node) Perform computation of one iteration of the algorithm on a given node using the epidemic label propagation algorithm.
void	setParameters(double m, double delta) Sets the preference exponent and hop attenuation factor to the given values.

Methods inherited from class org.graphstream.algorithm.community.DecentralizedCommunityAlgorithm

attributeChanged, compute, edgeAdded, edgeAttributeAdded, edgeAttributeChanged, edgeAttributeRemoved, edgeRemoved, getMarker, getRandom, graphAttributeAdded, graphAttributeChanged, graphAttributeRemoved, graphCleared, init, init, nodeAdded, nodeAttributeAdded, nodeAttributeChanged, nodeAttributeRemoved, nodeRemoved, setMarker, setRandom, staticMode, stepBegins, terminate

Methods inherited from class java.lang.Object

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

Constructor Detail

Leung

public Leung()

Leung

public Leung(org.graphstream.graph.Graph graph)

Leung

public Leung(org.graphstream.graph.Graph graph,
             String marker)

Leung

public Leung(org.graphstream.graph.Graph graph,
             String marker,
             String weightMarker)

Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the specified weightMarker to retrieve the weight attribute of graph edges.

Parameters:

graph - graph to which the algorithm will be applied

marker - community attribute marker

weightMarker - edge weight marker

Leung

public Leung(org.graphstream.graph.Graph graph,
             double m,
             double delta)

Create a new Leung algorithm instance, attached to the specified graph, using the default markers for the node community and edge weight attributes. Sets the preference exponent and hop attenuation factor to the given values.

Parameters:

graph - graph to which the algorithm will be applied

m - comparable function preference exponent value

delta - hop attenuation factor value

Leung

public Leung(org.graphstream.graph.Graph graph,
             String marker,
             double m,
             double delta)

Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the default marker to retrieve the weight attribute of graph edges. Sets the preference exponent and hop attenuation factor to the given values.

Parameters:

graph - graph to which the algorithm will be applied

marker - community attribute marker

m - comparable function preference exponent value

delta - hop attenuation factor value

Leung

public Leung(org.graphstream.graph.Graph graph,
             String marker,
             String weightMarker,
             double m,
             double delta)

Create a new Leung algorithm instance, attached to the specified graph, using the specified marker to store the community attribute, and the specified weightMarker to retrieve the weight attribute of graph edges. Sets the preference exponent and hop attenuation factor to the given values.

Parameters:

graph - graph to which the algorithm will be applied

marker - community attribute marker

weightMarker - edge weight marker

m - comparable function preference exponent value

delta - hop attenuation factor value

Method Detail

setParameters

public void setParameters(double m,
                          double delta)

Sets the preference exponent and hop attenuation factor to the given values.

Parameters:

m - comparable function preference exponent value

delta - hop attenuation factor value

computeNode

public void computeNode(org.graphstream.graph.Node node)

Description copied from class: EpidemicCommunityAlgorithm

Perform computation of one iteration of the algorithm on a given node using the epidemic label propagation algorithm.

Overrides:

computeNode in class EpidemicCommunityAlgorithm