Examples of EigenvectorCentralityPower

org.neo4j.graphalgo.impl.centrality.EigenvectorCentralityPower
Computing eigenvector centrality with the "power method". Convergence is dependent of the eigenvalues of the input adjacency matrix (the network). If the two largest eigenvalues are u1 and u2, a small factor u2/u1 will give a faster convergence (i.e. faster computation). NOTE: Currently only works on Doubles. @complexity The {@link CostEvaluator} is called once for every relationshipin each iteration. Assuming this is done in constant time, the total time complexity is O(i(n + m)) when i iterations are done. @author Patrik Larsson

Examples of org.neo4j.graphalgo.impl.centrality.EigenvectorCentralityPower

    @Override
    public EigenvectorCentrality getEigenvectorCentrality(
        Direction relationDirection, CostEvaluator<Double> costEvaluator,
        Set<Node> nodeSet, Set<Relationship> relationshipSet, double precision )
    {
        return new EigenvectorCentralityPower( relationDirection,
            costEvaluator, nodeSet, relationshipSet, precision );
    }

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