RandomNonUniformVertexSampling.java

/*
 * Copyright © 2014 - 2021 Leipzig University (Database Research Group)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.gradoop.flink.model.impl.operators.sampling;

import org.apache.flink.api.java.DataSet;
import org.gradoop.common.model.api.entities.Edge;
import org.gradoop.common.model.api.entities.GraphHead;
import org.gradoop.common.model.api.entities.Vertex;
import org.gradoop.flink.algorithms.gelly.vertexdegrees.DistinctVertexDegrees;
import org.gradoop.flink.model.api.epgm.BaseGraph;
import org.gradoop.flink.model.api.epgm.BaseGraphCollection;
import org.gradoop.flink.model.impl.functions.epgm.PropertyRemover;
import org.gradoop.flink.model.impl.operators.sampling.common.SamplingConstants;
import org.gradoop.flink.model.impl.operators.sampling.functions.AddMaxDegreeCrossFunction;
import org.gradoop.flink.model.impl.operators.sampling.functions.NonUniformVertexRandomFilter;
import org.gradoop.flink.model.impl.operators.sampling.functions.VertexToDegreeMap;

/**
 * Computes a vertex sampling of the graph (new graph head will be generated). Retains randomly
 * chosen vertices of a given relative amount and all edges which source- and target-vertices
 * were chosen. A degree-dependent value is taken into account to have a bias towards high-degree
 * vertices. There may retain some unconnected vertices in the sampled graph.
 *
 * @param <G>  The graph head type.
 * @param <V>  The vertex type.
 * @param <E>  The edge type.
 * @param <LG> The type of the graph.
 * @param <GC> The type of the graph collection.
 */
public class RandomNonUniformVertexSampling<
  G extends GraphHead,
  V extends Vertex,
  E extends Edge,
  LG extends BaseGraph<G, V, E, LG, GC>,
  GC extends BaseGraphCollection<G, V, E, LG, GC>> extends SamplingAlgorithm<G, V, E, LG, GC> {

  /**
   * Relative amount of vertices in the result graph
   */
  private final float sampleSize;

  /**
   * Seed for the random number generator
   * If seed is 0, the random generator is created without seed
   */
  private final long randomSeed;

  /**
   * Creates new RandomNonUniformVertexSampling instance.
   *
   * @param sampleSize relative sample size
   */
  public RandomNonUniformVertexSampling(float sampleSize) {
    this(sampleSize, 0L);
  }

  /**
   * Creates new RandomNonUniformVertexSampling instance.
   *
   * @param sampleSize relative sample size
   * @param randomSeed random seed value (can be 0)
   */
  public RandomNonUniformVertexSampling(float sampleSize, long randomSeed) {
    this.sampleSize = sampleSize;
    this.randomSeed = randomSeed;
  }

  @Override
  public LG sample(LG graph) {

    graph = graph.callForGraph(new DistinctVertexDegrees<>(
      SamplingConstants.DEGREE_PROPERTY_KEY,
      SamplingConstants.IN_DEGREE_PROPERTY_KEY,
      SamplingConstants.OUT_DEGREE_PROPERTY_KEY,
      true));

    DataSet<V> newVertices = graph.getVertices()
      .map(new VertexToDegreeMap<>(SamplingConstants.DEGREE_PROPERTY_KEY))
      .max(0)
      .cross(graph.getVertices())
      .with(new AddMaxDegreeCrossFunction<>(SamplingConstants.PROPERTY_KEY_MAX_DEGREE));

    graph = graph.getFactory()
      .fromDataSets(graph.getGraphHead(), newVertices, graph.getEdges());

    newVertices = graph.getVertices().filter(new NonUniformVertexRandomFilter<>(
      sampleSize,
      randomSeed,
      SamplingConstants.DEGREE_PROPERTY_KEY,
      SamplingConstants.PROPERTY_KEY_MAX_DEGREE));

    newVertices = newVertices
      .map(new PropertyRemover<>(SamplingConstants.DEGREE_PROPERTY_KEY))
      .map(new PropertyRemover<>(SamplingConstants.IN_DEGREE_PROPERTY_KEY))
      .map(new PropertyRemover<>(SamplingConstants.OUT_DEGREE_PROPERTY_KEY))
      .map(new PropertyRemover<>(SamplingConstants.PROPERTY_KEY_MAX_DEGREE));

    return graph.getFactory().fromDataSets(newVertices, graph.getEdges()).verify();
  }
}