Class VoltageClusterer<V,E>

java.lang.Object
edu.uci.ics.jung.algorithms.cluster.VoltageClusterer<V,E>

public class VoltageClusterer<V,E> extends Object

Clusters vertices of a Graph based on their ranks as calculated by VoltageScorer. This algorithm is based on, but not identical with, the method described in the paper below. The primary difference is that Wu and Huberman assume a priori that the clusters are of approximately the same size, and therefore use a more complex method than k-means (which is used here) for determining cluster membership based on co-occurrence data.

The algorithm proceeds as follows:

  • first, generate a set of candidate clusters as follows:
    • pick (widely separated) vertex pair, run VoltageScorer
    • group the vertices in two clusters according to their voltages
    • store resulting candidate clusters
  • second, generate k-1 clusters as follows:
    • pick a vertex v as a cluster 'seed'
      (Wu/Huberman: most frequent vertex in candidate clusters)
    • calculate co-occurrence over all candidate clusters of v with each other vertex
    • separate co-occurrence counts into high/low; high vertices constitute a cluster
    • remove v's vertices from candidate clusters; continue
  • finally, remaining unassigned vertices are assigned to the kth ("garbage") cluster.

NOTE: Depending on how the co-occurrence data splits the data into clusters, the number of clusters returned by this algorithm may be less than the number of clusters requested. The number of clusters will never be more than the number requested, however.

See Also:
  • "'Finding communities in linear time: a physics approach', Fang Wu and Bernardo Huberman, http://www.hpl.hp.com/research/idl/papers/linear/"
  • VoltageScorer
  • KMeansClusterer
  • Field Details

  • Constructor Details

    • VoltageClusterer

      public VoltageClusterer(Graph<V,E> g, int num_candidates)
      Creates an instance of a VoltageCluster with the specified parameters. These are mostly parameters that are passed directly to VoltageScorer and KMeansClusterer.
      Parameters:
      g - the graph whose vertices are to be clustered
      num_candidates - the number of candidate clusters to create
  • Method Details

    • setRandomSeed

      protected void setRandomSeed(int random_seed)
    • getCommunity

      public Collection<Set<V>> getCommunity(V v)
      Parameters:
      v - the vertex whose community we wish to discover
      Returns:
      a community (cluster) centered around v.
    • cluster

      public Collection<Set<V>> cluster(int num_clusters)
      Clusters the vertices of g into num_clusters clusters, based on their connectivity.
      Parameters:
      num_clusters - the number of clusters to identify
      Returns:
      a collection of clusters (sets of vertices)
    • cluster_internal

      protected Collection<Set<V>> cluster_internal(V origin, int num_clusters)
      Does the work of getCommunity and cluster.
      Parameters:
      origin - the vertex around which clustering is to be done
      num_clusters - the (maximum) number of clusters to find
      Returns:
      a collection of clusters (sets of vertices)
    • addTwoCandidateClusters

      protected void addTwoCandidateClusters(LinkedList<Set<V>> candidates, Map<V,double[]> voltage_ranks)
      Do k-means with three intervals and pick the smaller two clusters (presumed to be on the ends); this is closer to the Wu-Huberman method.
      Parameters:
      candidates - the list of clusters to populate
      voltage_ranks - the voltage values for each vertex
    • addOneCandidateCluster

      protected void addOneCandidateCluster(LinkedList<Set<V>> candidates, Map<V,double[]> voltage_ranks)
      alternative to addTwoCandidateClusters(): cluster vertices by voltages into 2 clusters. We only consider the smaller of the two clusters returned by k-means to be a 'true' cluster candidate; the other is a garbage cluster.
      Parameters:
      candidates - the list of clusters to populate
      voltage_ranks - the voltage values for each vertex
    • getSeedCandidates

      protected List<V> getSeedCandidates(Collection<Set<V>> candidates)
      Returns a list of cluster seeds, ranked in decreasing order of number of appearances in the specified collection of candidate clusters.
      Parameters:
      candidates - the set of candidate clusters
      Returns:
      a set of cluster seeds
    • getObjectCounts

      protected Map<V,double[]> getObjectCounts(Collection<Set<V>> candidates, V seed)