All Classes

Class	Description
ABCFormat	Utilities for handling the ABC (source, target, weight) edge list format.
Application	Watset command-line interface.
Application.ParseStatus	Status of command-line argument parsing.
CachedNormalizedModifiedPurity<V>	Cached normalized modified purity evaluation measure for overlapping clustering.
ChineseWhispers<V,E>	Implementation of the Chinese Whispers algorithm.
ChineseWhispers.Builder<V,E>	Builder for ChineseWhispers.
ChineseWhispers.Implementation<V,E>	Actual implementation of Chinese Whispers.
ClusteringAlgorithmBuilder<V,E,C extends org.jgrapht.alg.interfaces.ClusteringAlgorithm<V>>	A builder for constructing the ClusteringAlgorithm instances.
ClusteringAlgorithmProvider<V,E>	A utility class that creates instances of the graph clustering algorithms.
ClusteringAlgorithmProvider.ProvidingAlgorithm	Clustering algorithms that ClusteringAlgorithmProvider knows how to provide.
ComponentsClustering<V,E>	A trivial clustering algorithm that treats every connected component as a cluster.
ComponentsClustering.Builder<V,E>	Builder for ComponentsClustering.
EmptyClustering<V>	A trivial clustering algorithm that returns no clusters.
EmptyClustering.Builder<V,E>	Builder for EmptyClustering.
ILEFormat	Utilities for handling the ILE (identifier, length, elements) file format.
IndexedSense<V>	An integer sense identifier.
MarkovClustering<V,E>	Naïve implementation of the Markov Clustering (MCL) algorithm.
MarkovClustering.Builder<V,E>	Builder for MarkovClustering.
MarkovClustering.Implementation<V,E>	Actual implementation of Markov Clustering.
MarkovClusteringExternal<V,E>	A wrapper for the official implementation of the Markov Clustering (MCL) algorithm in C.
MarkovClusteringExternal.Builder<V,E>	Builder for MarkovClusteringExternal.
MarkovClusteringExternal.Implementation<V,E>	Actual implementation of the Markov Clustering wrapper.
Matrices	Utilities for working with matrices.
Matrices.ColumnNormalizeVisitor	Visitor that normalizes columns.
Matrices.ColumnSumVisitor	Visitor that computes column sums.
Matrices.InflateVisitor	Visitor that raises each element to the specified power.
Matrices.RowNormalizeVisitor	Visitor that normalizes rows.
Maximizer	Utilities for searching arguments of the maxima of the function.
MaxMax<V,E>	Implementation of the MaxMax soft clustering algorithm.
MaxMax.Builder<V,E>	Builder for MaxMax.
MaxMax.Implementation<V,E>	Actual implementation of MaxMax.
MaxMaxClustering<V>	A MaxMax clustering.
MaxMaxClustering.MaxMaxClusteringImpl<V>	Default implementation of the MaxMax clustering.
Measurer<V,E>	A clustering algorithm performance measurement class.
Measurer.Measurement<V>	A performance measurement result.
Neighbors	Utilities for extracting neighborhood graphs and iterating over them.
NetworkXFormat	Utilities for handling pickled NetworkX graphs.
NodeEmbedding<V>	Coordinates of the graph node.
NodeWeighting<V,E>	Node weighting for Chinese Whispers.
NodeWeightings	Useful implementations of NodeWeighting.
NodeWeightings.LabelNodeWeighting<V,E>	A trivial and not particularly useful node weighting approach that assigns the current node label as the weight.
NodeWeightings.LinearNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood divided by the neighbor node degree.
NodeWeightings.LogNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood divided by the logarithm of the neighbor node degree.
NodeWeightings.TopNodeWeighting<V,E>	The node weighting approach that chooses the label with the highest total edge weight in the neighborhood.
NodeWeightings.WeightingMode	Weighting modes.
NormalizedModifiedPurity<V>	Normalized modified purity evaluation measure for overlapping clustering.
Pairwise<V>	Pairwise precision, recall, and F-score for cluster evaluation.
PrecisionRecall	A wrapper for precision and recall values that computes F-score.
Sampling	Utilities for statistical evaluation of computational experiments.
Sense<V>	A monad that provides the wrapped value with a sense identifier.
SenseInduction<V,E>	A simple graph-based word sense induction approach that clusters node neighborhoods.
SingletonClustering<V,E>	A trivial clustering algorithm that puts every node in a separate cluster.
SingletonClustering.Builder<V,E>	Builder for SingletonClustering.
SpectralClustering<V,E>	Spectral Clustering performs clustering of the graph's Spectral Embedding.
SpectralClustering.Builder<V,E>	Builder for SpectralClustering.
SpectralClustering.Implementation<V,E>	Actual implementation of Spectral Clustering.
TogetherClustering<V,E>	A trivial clustering algorithm that puts every node together in a single large cluster.
TogetherClustering.Builder<V,E>	Builder for TogetherClustering.
Watset<V,E>	Watset is a local-global meta-algorithm for fuzzy graph clustering.
Watset.Builder<V,E>	Builder for Watset.
Watset.Implementation<V,E>	Actual implementation of Simplified Watset.
WatsetClustering<V>	A Watset clustering.
WatsetClustering.WatsetClusteringImpl<V>	A Watset clustering that computes disambiguated contexts on demand.
Word2VecFormat	Utilities for handling the Word2Vec text format for node embeddings.
Word2VecFormat.SpaceStrategy	Space character handling strategy.