/*
* Copyright (c) 2003, the JUNG Project and the Regents of the University
* of California
* All rights reserved.
*
* This software is open-source under the BSD license; see either
* "license.txt" or
* http://jung.sourceforge.net/license.txt for a description.
*/
package edu.uci.ics.jung.algorithms.cluster;
import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.commons.collections15.Transformer;
import edu.uci.ics.jung.algorithms.scoring.BetweennessCentrality;
import edu.uci.ics.jung.graph.Graph;
import edu.uci.ics.jung.graph.util.Pair;
/**
* An algorithm for computing clusters (community structure) in graphs based on
* edge betweenness. The betweenness of an edge is defined as the extent to
* which that edge lies along shortest paths between all pairs of nodes.
*
* This algorithm works by iteratively following the 2 step process:
* <ul>
* <li>Compute edge betweenness for all edges in current graph
* <li>Remove edge with highest betweenness
* </ul>
* <p>
* Running time is: O(kmn) where k is the number of edges to remove, m is the
* total number of edges, and n is the total number of vertices. For very sparse
* graphs the running time is closer to O(kn^2) and for graphs with strong
* community structure, the complexity is even lower.
* <p>
* This algorithm is a slight modification of the algorithm discussed below in
* that the number of edges to be removed is parameterized.
*
* @author Scott White
* @author Tom Nelson (converted to jung2)
* @see "Community structure in social and biological networks by Michelle Girvan and Mark Newman"
*/
public class EdgeBetweennessClusterer<V, E>
implements Transformer<Graph<V, E>, Set<Set<V>>> {
private int mNumEdgesToRemove;
private Map<E, Pair<V>> edges_removed;
/**
* Constructs a new clusterer for the specified graph.
*
* @param numEdgesToRemove
* the number of edges to be progressively removed from the graph
*/
public EdgeBetweennessClusterer(int numEdgesToRemove) {
mNumEdgesToRemove = numEdgesToRemove;
edges_removed = new LinkedHashMap<E, Pair<V>>();
}
/**
* Finds the set of clusters which have the strongest "community structure".
* The more edges removed the smaller and more cohesive the clusters.
*
* @param graph
* the graph
*/
@Override
public Set<Set<V>> transform(Graph<V, E> graph) {
if (mNumEdgesToRemove < 0 || mNumEdgesToRemove > graph.getEdgeCount()) {
throw new IllegalArgumentException(
"Invalid number of edges passed in.");
}
edges_removed.clear();
for (int k = 0; k < mNumEdgesToRemove; k++) {
BetweennessCentrality<V, E> bc = new BetweennessCentrality<V, E>(
graph);
E to_remove = null;
double score = 0;
for (E e : graph.getEdges()) {
if (bc.getEdgeScore(e) > score) {
to_remove = e;
score = bc.getEdgeScore(e);
}
}
edges_removed.put(to_remove, graph.getEndpoints(to_remove));
graph.removeEdge(to_remove);
}
WeakComponentClusterer<V, E> wcSearch = new WeakComponentClusterer<V, E>();
Set<Set<V>> clusterSet = wcSearch.transform(graph);
for (Map.Entry<E, Pair<V>> entry : edges_removed.entrySet()) {
Pair<V> endpoints = entry.getValue();
graph.addEdge(entry.getKey(), endpoints.getFirst(),
endpoints.getSecond());
}
return clusterSet;
}
/**
* Retrieves the list of all edges that were removed (assuming extract(...)
* was previously called). The edges returned are stored in order in which
* they were removed.
*
* @return the edges in the original graph
*/
public List<E> getEdgesRemoved() {
return new ArrayList<E>(edges_removed.keySet());
}
}