+++ /dev/null
-/*
- * Created on Jul 12, 2007
- *
- * Copyright (c) 2007, 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.scoring;
-
-import org.apache.commons.collections15.Transformer;
-
-import edu.uci.ics.jung.graph.Hypergraph;
-
-/**
- * Calculates eigenvector centrality for each vertex in the graph.
- * The 'eigenvector centrality' for a vertex is defined as the fraction of
- * time that a random walk(er) will spend at that vertex over an infinite
- * time horizon.
- * Assumes that the graph is strongly connected.
- */
-public class EigenvectorCentrality<V,E> extends PageRank<V,E>
-{
- /**
- * Creates an instance with the specified graph and edge weights.
- * The outgoing edge weights for each edge must sum to 1.
- * (See <code>UniformDegreeWeight</code> for one way to handle this for
- * undirected graphs.)
- * @param graph the graph for which the centrality is to be calculated
- * @param edge_weights the edge weights
- */
- public EigenvectorCentrality(Hypergraph<V,E> graph,
- Transformer<E, ? extends Number> edge_weights)
- {
- super(graph, edge_weights, 0);
- acceptDisconnectedGraph(false);
- }
-
- /**
- * Creates an instance with the specified graph and default edge weights.
- * (Default edge weights: <code>UniformDegreeWeight</code>.)
- * @param graph the graph for which the centrality is to be calculated.
- */
- public EigenvectorCentrality(Hypergraph<V,E> graph)
- {
- super(graph, 0);
- acceptDisconnectedGraph(false);
- }
-}