Uses of Class
edu.uci.ics.jung.algorithms.IterativeProcess

Packages that use IterativeProcess
edu.uci.ics.jung.algorithms.flows   
edu.uci.ics.jung.algorithms.importance   
 

Uses of IterativeProcess in edu.uci.ics.jung.algorithms.flows
 

Subclasses of IterativeProcess in edu.uci.ics.jung.algorithms.flows
 class EdmondsKarpMaxFlow
          Implements the EdmondsKarpMaxFlow algorithm for solving the maximum flow problem.
 

Uses of IterativeProcess in edu.uci.ics.jung.algorithms.importance
 

Subclasses of IterativeProcess in edu.uci.ics.jung.algorithms.importance
 class AbstractRanker
          Abstract class for algorithms that rank nodes or edges by some "importance" metric.
 class BetweennessCentrality
          Computes betweenness centrality for each vertex and edge in the graph.
 class DegreeDistributionRanker
          A simple node importance ranker based on the degree of the node.
 class HITS
          Calculates the "hubs-and-authorities" importance measures for each node in a graph.
 class HITSWithPriors
          Algorithm that extends the HITS algorithm by incorporating root nodes (priors).
 class KStepMarkov
          Algorithm variant of PageRankWithPriors that computes the importance of a node based upon taking fixed-length random walks out from the root set and then computing the stationary probability of being at each node.
 class MarkovCentrality
           
 class PageRank
          This algorithm measures the importance of a node in terms of the fraction of time spent at that node relative to all other nodes.
 class PageRankWithPriors
          Algorithm that extends the PageRank algorithm by incorporating root nodes (priors).
 class RandomWalkBetweenness
          Computes betweenness centrality for each vertex in the graph.
 class RandomWalkSTBetweenness
          /** Computes s-t betweenness centrality for each vertex in the graph.
 class RelativeAuthorityRanker
          This class provides basic infrastructure for relative authority algorithms that compute the importance of nodes relative to one or more root nodes.
 class WeightedNIPaths
          This algorithm measures the importance of nodes based upon both the number and length of disjoint paths that lead to a given node from each of the nodes in the root set.