Java tutorial
/* * 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.Collection; import java.util.HashSet; import java.util.Set; import org.apache.commons.collections15.Buffer; import org.apache.commons.collections15.Transformer; import org.apache.commons.collections15.buffer.UnboundedFifoBuffer; import edu.uci.ics.jung.graph.Graph; /** * Finds all weak components in a graph as sets of vertex sets. A weak component is defined as * a maximal subgraph in which all pairs of vertices in the subgraph are reachable from one * another in the underlying undirected subgraph. * <p>This implementation identifies components as sets of vertex sets. * To create the induced graphs from any or all of these vertex sets, * see <code>algorithms.filters.FilterUtils</code>. * <p> * Running time: O(|V| + |E|) where |V| is the number of vertices and |E| is the number of edges. * @author Scott White */ public class WeakComponentClusterer<V, E> implements Transformer<Graph<V, E>, Set<Set<V>>> { /** * Extracts the weak components from a graph. * @param graph the graph whose weak components are to be extracted * @return the list of weak components */ public Set<Set<V>> transform(Graph<V, E> graph) { Set<Set<V>> clusterSet = new HashSet<Set<V>>(); HashSet<V> unvisitedVertices = new HashSet<V>(graph.getVertices()); while (!unvisitedVertices.isEmpty()) { Set<V> cluster = new HashSet<V>(); V root = unvisitedVertices.iterator().next(); unvisitedVertices.remove(root); cluster.add(root); Buffer<V> queue = new UnboundedFifoBuffer<V>(); queue.add(root); while (!queue.isEmpty()) { V currentVertex = queue.remove(); Collection<V> neighbors = graph.getNeighbors(currentVertex); for (V neighbor : neighbors) { if (unvisitedVertices.contains(neighbor)) { queue.add(neighbor); unvisitedVertices.remove(neighbor); cluster.add(neighbor); } } } clusterSet.add(cluster); } return clusterSet; } }