- All Implemented Interfaces:
- VertexScorer<V,Double>, IterativeContext
public class EigenvectorCentrality<V,E>
- extends PageRank<V,E>
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
Assumes that the graph is strongly connected.
|Methods inherited from class edu.uci.ics.jung.algorithms.scoring.AbstractIterativeScorer
acceptDisconnectedGraph, done, evaluate, getAdjustedIncidentCount, getCurrentValue, getEdgeWeight, getEdgeWeights, getIterations, getMaxIterations, getOutputValue, getTolerance, getVertexScore, isDisconnectedGraphOK, setCurrentValue, setEdgeWeights, setHyperedgesAreSelfLoops, setMaxIterations, setOutputValue, setTolerance, step, swapOutputForCurrent, updateMaxDelta
|Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public EigenvectorCentrality(Hypergraph<V,E> graph,
- Creates an instance with the specified graph and edge weights.
The outgoing edge weights for each edge must sum to 1.
UniformDegreeWeight for one way to handle this for
graph - the graph for which the centrality is to be calculated
edge_weights - the edge weights
public EigenvectorCentrality(Hypergraph<V,E> graph)
- Creates an instance with the specified graph and default edge weights.
(Default edge weights:
graph - the graph for which the centrality is to be calculated.
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