@Override public AverageClusteringCoefficient<K, VV, EV> run(Graph<K, VV, EV> input) throws Exception { super.run(input); DataSet<LocalClusteringCoefficient.Result<K>> localClusteringCoefficient = input .run(new LocalClusteringCoefficient<K, VV, EV>() .setParallelism(parallelism)); averageClusteringCoefficientHelper = new AverageClusteringCoefficientHelper<>(); localClusteringCoefficient .output(averageClusteringCoefficientHelper) .name("Average clustering coefficient"); return this; }
@Override public AverageClusteringCoefficient<K, VV, EV> run(Graph<K, VV, EV> input) throws Exception { super.run(input); DataSet<LocalClusteringCoefficient.Result<K>> localClusteringCoefficient = input .run(new LocalClusteringCoefficient<K, VV, EV>() .setLittleParallelism(littleParallelism)); averageClusteringCoefficientHelper = new AverageClusteringCoefficientHelper<>(); localClusteringCoefficient .output(averageClusteringCoefficientHelper) .name("Average clustering coefficient"); return this; }
@Override public AverageClusteringCoefficient<K, VV, EV> run(Graph<K, VV, EV> input) throws Exception { super.run(input); DataSet<LocalClusteringCoefficient.Result<K>> localClusteringCoefficient = input .run(new LocalClusteringCoefficient<K, VV, EV>() .setParallelism(parallelism)); averageClusteringCoefficientHelper = new AverageClusteringCoefficientHelper<>(); localClusteringCoefficient .output(averageClusteringCoefficientHelper) .name("Average clustering coefficient"); return this; }
@Override public AverageClusteringCoefficient<K, VV, EV> run(Graph<K, VV, EV> input) throws Exception { super.run(input); DataSet<LocalClusteringCoefficient.Result<K>> localClusteringCoefficient = input .run(new LocalClusteringCoefficient<K, VV, EV>() .setParallelism(parallelism)); averageClusteringCoefficientHelper = new AverageClusteringCoefficientHelper<>(); localClusteringCoefficient .output(averageClusteringCoefficientHelper) .name("Average clustering coefficient"); return this; }