Class KStepMarkov<V,E>

All Implemented Interfaces:
VertexScorer<V,Double>, IterativeContext

public class KStepMarkov<V,E> extends PageRankWithPriors<V,E>
A special case of PageRankWithPriors in which the final scores represent a probability distribution over position assuming a random (Markovian) walk of exactly k steps, based on the initial distribution specified by the priors.

NOTE: The version of KStepMarkov in algorithms.importance (and in JUNG 1.x) is believed to be incorrect: rather than returning a score which represents a probability distribution over position assuming a k-step random walk, it returns a score which represents the sum over all steps of the probability for each step. If you want that behavior, set the 'cumulative' flag as follows before calling evaluate():

     KStepMarkov ksm = new KStepMarkov(...);
     ksm.setCumulative(true);
     ksm.evaluate();
 
By default, the 'cumulative' flag is set to false. NOTE: THIS CLASS IS NOT YET COMPLETE. USE AT YOUR OWN RISK. (The original behavior is captured by the version still available in algorithms.importance.)
See Also:
  • Field Details

    • cumulative

      private boolean cumulative
  • Constructor Details

    • KStepMarkov

      public KStepMarkov(Hypergraph<V,E> graph, com.google.common.base.Function<E,? extends Number> edge_weights, com.google.common.base.Function<V,Double> vertex_priors, int steps)
      Creates an instance based on the specified graph, edge weights, vertex priors (initial scores), and number of steps to take.
      Parameters:
      graph - the input graph
      edge_weights - the edge weights (transition probabilities)
      vertex_priors - the initial probability distribution (score assignment)
      steps - the number of times that step() will be called by evaluate
    • KStepMarkov

      public KStepMarkov(Hypergraph<V,E> graph, com.google.common.base.Function<V,Double> vertex_priors, int steps)
      Creates an instance based on the specified graph, vertex priors (initial scores), and number of steps to take. The edge weights (transition probabilities) are set to default values (a uniform distribution over all outgoing edges).
      Parameters:
      graph - the input graph
      vertex_priors - the initial probability distribution (score assignment)
      steps - the number of times that step() will be called by evaluate
    • KStepMarkov

      public KStepMarkov(Hypergraph<V,E> graph, int steps)
      Creates an instance based on the specified graph and number of steps to take. The edge weights (transition probabilities) and vertex initial scores (prior probabilities) are set to default values (a uniform distribution over all outgoing edges, and a uniform distribution over all vertices, respectively).
      Parameters:
      graph - the input graph
      steps - the number of times that step() will be called by evaluate
  • Method Details

    • initialize

      private void initialize(int steps)
    • setCumulative

      public void setCumulative(boolean cumulative)
      Specifies whether this instance should assign a score to each vertex based on the sum over all steps of the probability for each step. See the class-level documentation for details.
      Parameters:
      cumulative - true if this instance should assign a cumulative score to each vertex
    • update

      public double update(V v)
      Updates the value for this vertex. Called by step().
      Overrides:
      update in class PageRankWithPriors<V,E>
      Parameters:
      v - the vertex whose value is to be updated
      Returns:
      the updated value