Uses of Class
netkit.graph.Graph

Packages that use Graph

netkit
netkit.classifiers
netkit.classifiers.aggregators
netkit.classifiers.io
netkit.classifiers.nonrelational
netkit.classifiers.relational
netkit.graph
netkit.graph.edgecreator
netkit.graph.io
netkit.inference
netkit.util

Uses of Graph in netkit

Methods in netkit that return Graph

Graph

EdgeTransformer.readEdges(java.io.Reader reader, EdgeType edgeType)

Methods in netkit with parameters of type Graph

void	GraphStat.printDegreeDistribution(Graph graph)
void	GraphStat.printGlobalInfo(Graph graph)
void	GraphStat.printNodeStatistics(Graph graph)
static void	EdgeTransformer.pruneLess(Graph graph, EdgeType edgeType, double pruneless)
static void	EdgeTransformer.pruneMinK(Graph graph, EdgeType edgeType, int minK, boolean reverse)
static void	EdgeTransformer.pruneMore(Graph graph, EdgeType edgeType, double prunemore)
static void	EdgeTransformer.pruneTopK(Graph graph, EdgeType edgeType, int topK, boolean reverse)
static void	EdgeTransformer.reweight(Graph graph, Edge[] edges, double reweight)
static void	EdgeTransformer.reweight(Graph graph, EdgeType edgeType, double reweight)
void	GraphStat.run(Graph graph)
void	GraphStat.saveClusters(Graph graph)
void	GraphStat.saveComponents(Graph graph)
void	GraphStat.saveGraph(Graph graph)
void	EdgeTransformer.transform(Graph graph, EdgeType edgeType)

Uses of Graph in netkit.classifiers

Fields in netkit.classifiers declared as Graph

protected Graph

ClassifierImp.graph

Methods in netkit.classifiers that return Graph

static Graph	DataSampler.buildGraph()
Graph	NetworkLearning.getGraph()
Graph	NetworkLearner.getGraph()
Graph	Estimate.getGraph()
Graph	DataView.getGraph()
Graph	Classification.getGraph()

Methods in netkit.classifiers with parameters of type Graph

void	ClassifierImp.induceModel(Graph graph, DataSplit split)
void	Classifier.induceModel(Graph graph, DataSplit split)
void	NetworkLearning.setGraph(Graph g)

Constructors in netkit.classifiers with parameters of type Graph

Classification(Graph graph, java.lang.String nodeType, AttributeCategorical attribute)

DataView(Graph g, java.lang.String nodeType, AttributeCategorical attrib)

DataView(Graph g, java.lang.String nodeType, AttributeCategorical attrib, long seed)

DataView(Graph g, java.lang.String nodeType, AttributeCategorical attrib, long seed, boolean replacement, boolean stratified, boolean pruneZeroKnowledge)

DataView(Graph g, java.lang.String nodeType, AttributeCategorical attrib, long seed, boolean replacement, boolean stratified, boolean pruneZeroKnowledge, boolean pruneSingletons, boolean sampleUnknown)

Estimate(Graph graph, java.lang.String nodeType, AttributeCategorical attribute)

Uses of Graph in netkit.classifiers.aggregators

Methods in netkit.classifiers.aggregators with parameters of type Graph

static void

SharedNodeInfo.initialize(Graph g) Assume that we will be doing aggregation over this particular graph until further notice

Uses of Graph in netkit.classifiers.io

Methods in netkit.classifiers.io with parameters of type Graph

void	ReadClassificationGeneric.readClassification(Graph graph, Classification labels, java.io.File input) Reads in a set of classifications from the given file, assuming that each line is of the form 'nodeID,class'.
void	ReadClassification.readClassification(Graph graph, Classification labels, java.io.File input) Read from a given file a estimate of classifications for nodes in the given graph.
Classification	ReadClassificationGeneric.readClassification(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Creates a new Classification object based on the graph, nodeType and attribute and then calls the generic readClassification method.
Classification	ReadClassification.readClassification(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Read from a given file a estimate of classifications for nodes in the given graph.
void	ReadEstimateRainbow.readEstimate(Graph graph, Estimate estimates, java.io.File input) Reads in a set of estimates from the given file, assuming that each line is of the form: nodeID trueclass class:score ...
void	ReadEstimate.readEstimate(Graph graph, Estimate estimates, java.io.File input) Read from a given file estimates for nodes in the given graph.
Estimate	ReadEstimateRainbow.readEstimate(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Create a new Estimate object and call the more general readEstimate method.
Estimate	ReadEstimate.readEstimate(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Read from a given file an estimate for nodes in the given graph.

Uses of Graph in netkit.classifiers.nonrelational

Methods in netkit.classifiers.nonrelational with parameters of type Graph

void	UniformPrior.induceModel(Graph graph, DataSplit split) Makes a uniform prediction array---all classes are equally likely
void	MetaMultiplicative.induceModel(Graph graph, DataSplit split) Induce the model.
void	LocalWeka.induceModel(Graph graph, DataSplit split) Induce the weka classifier by creating a training Instances object according to the schema of the nodes to be classified.
void	LocalMetaClassifier.induceModel(Graph graph, DataSplit split) This induces each of the local classifiers individually.
void	ExternalPrior.induceModel(Graph graph, DataSplit split) Inducing this model simply means to read the estimates from the input file.

Uses of Graph in netkit.classifiers.relational

Methods in netkit.classifiers.relational with parameters of type Graph

void	WeightedVoteRelationalNeighbor.induceModel(Graph graph, DataSplit split) wvRN has no model, so this only initializes what needs to be done for laplace correction (in addition to whatever the superclass does).
void	NetworkWeka.induceModel(Graph graph, DataSplit split) Induce the weka classifier by creating a training Instances object according to the schema of the nodes to be classified.
void	NetworkOnlyBayes.induceModel(Graph graph, DataSplit split) Induce the model by computing the counts for Prob(classIdx | neighborClassIdx)
void	NetworkMetaClassifier.induceModel(Graph graph, DataSplit split) This separately induces all the non-relational and relational classifiers in addition to any setup the super-class needs to do.
void	NetworkClassifierImp.induceModel(Graph graph, DataSplit split) This method induces a new prediction model.
void	MetaMultiplicative.induceModel(Graph graph, DataSplit split) Induce the model.
void	Harmonic.induceModel(Graph graph, DataSplit split) Harmonic has no model per se as its learning consists of computing the harmonic function which results in the predictions.
void	ClassDistribRelNeighbor.induceModel(Graph graph, DataSplit split) Induce the cdRN model by finding the 'prototypical' neighborhood for each class of nodes.

Uses of Graph in netkit.graph

Fields in netkit.graph declared as Graph

protected Graph

AbstractAttributeMetaInfo.graph

Methods in netkit.graph that return Graph

Graph	Graph.clone()
Graph	Graph.subGraph(java.util.Collection<Node> nodeSet) Create a sub-graph consisting only of the given nodes and the edges between those nodes.

Constructors in netkit.graph with parameters of type Graph

AttributeCategoricalMetaInfo(AttributeCategorical attrib, Attributes attributes, Graph graph)
Construct an object of this type.

AttributeMetaInfo(Attribute attrib, Attributes attributes, Graph graph)
Construct an object of this type.

Uses of Graph in netkit.graph.edgecreator

Fields in netkit.graph.edgecreator declared as Graph

protected Graph

EdgeCreatorImp.graph

Methods in netkit.graph.edgecreator with parameters of type Graph

void	NormalizedNumericEdgeCreator.initialize(Graph graph, java.lang.String nodeType, int attributeIndex, double attributeValue, int maxEdges)
void	EdgeCreatorImp.initialize(Graph graph, java.lang.String nodeType, int attributeIndex, double attributeValue, int maxEdges)
void	EdgeCreator.initialize(Graph graph, java.lang.String nodeType, int attributeIndex, double attributeValue, int maxEdges) Initialize this creator.
void	BaseCategoricalEdgeCreator.initialize(Graph graph, java.lang.String nodeType, int attributeIndex, double attributeValue, int maxEdges)

Uses of Graph in netkit.graph.io

Methods in netkit.graph.io that return Graph

static Graph	SchemaReader.readGDASchema(java.io.File nodeFile, java.io.File edgeFile) Overloaded entry point for SchemaReader.readGDASchema(Reader,Reader)
static Graph	SchemaReader.readGDASchema(java.io.Reader nodeReader, java.io.Reader edgeReader) Reads the Node and Edge information from GDA formatted input, constructs the data structures and instantiates all of the instance data.
static Graph	PajekGraph.readGraph(java.io.File pajekFile) Overloaded entry point for PajekGraph.readGraph(Reader)
static Graph	NetkitGraph.readGraph(java.io.File schemafile)
static Graph	PajekGraph.readGraph(java.io.Reader pajekReader) Reads the Graph information from Pajek formatted input, constructs the data structures and instantiates all of the instance data.
static Graph	SchemaReader.readSchema(java.io.File file) Overloaded entry point for SchemaReader.readSchema(Reader,String)
static Graph	SchemaReader.readSchema(java.io.Reader reader, java.lang.String parentDirectory) Reads the Graph information from a schema file, constructs the data structures and instantiates all of the instance data.
static Graph	SchemaReader.stressTest(int numFields, int numNodes, int numEdges) This method conducts a stress test by creating a set of random nodes and edges and performs busy-work accessing the node and edge information.

Methods in netkit.graph.io with parameters of type Graph

static void	NetkitGraph.printNetKitEdges(Graph graph, java.io.PrintWriter pw, java.lang.String edgeName)
static void	NetkitGraph.printNetKitNodes(Graph graph, java.io.PrintWriter pw, java.lang.String nodeType)
static void	NetkitGraph.printNetKitNodes(Graph graph, java.io.PrintWriter pw, java.lang.String nodeType, boolean saveAttributes, boolean appendStatistics)
static void	EdgeReaderGDA.readEdges(java.io.Reader reader, Graph graph, EdgeType et) Reads Edges from the supplied Reader and creates the corresponding Edges in the Graph; Edges are validated by the supplied EdgeType which must have identical source and destination Node types, and the Nodes these Edges refer to must already exist in the Graph.
static void	EdgeReaderRN.readEdges(java.io.Reader reader, Graph graph, EdgeType et1, EdgeType et2) Reads Edges from the supplied Reader and creates the corresponding Edges in the Graph; Edges are validated by the the supplied EdgeTypes and the Nodes these Edges refer to must already exist in the Graph.
static void	NodeReader.readNodes(Graph graph, java.lang.String nodeType, java.io.Reader reader, boolean skipFirstLine) This static method does the work of reading input data for the class.
static void	PajekGraph.saveGraph(Graph graph, java.io.PrintWriter pw)
static void	DotGraph.saveGraph(Graph graph, java.io.PrintWriter pw)
static void	PajekGraph.saveGraph(Graph graph, java.io.PrintWriter pw, Classification truth, Estimate pred, java.lang.String labelAttribute)
static void	PajekGraph.saveGraph(Graph graph, java.lang.String file)
static void	NetkitGraph.saveGraph(Graph graph, java.lang.String outputStem)
static void	DotGraph.saveGraph(Graph graph, java.lang.String file)
static void	NetkitGraph.saveGraph(Graph graph, java.lang.String outputStem, boolean saveAttributes, boolean appendStatistics)
static void	PajekGraph.saveGraph(Graph graph, java.lang.String file, Classification truth, Estimate pred, java.lang.String labelAttribute) Save the given graph as a pajek graph, to the given file, using the classification and estimate and label.
static void	NetkitGraph.saveNetKitEdges(Graph graph, java.lang.String prefix, java.lang.String edgeName)
static void	NetkitGraph.saveNetKitNodes(Graph graph, java.lang.String prefix, java.lang.String nodeType)
static void	NetkitGraph.saveNetKitNodes(Graph graph, java.lang.String prefix, java.lang.String nodeType, boolean saveAttributes, boolean appendStatistics)
static void	SchemaWriter.writeSchema(Graph graph, java.io.Writer writer, java.util.Map<java.lang.String,java.lang.String> nodeTypeFiles, java.util.Map<java.lang.String,java.lang.String> edgeTypeFiles) Writes the Graph information in an extended ARFF format.

Uses of Graph in netkit.inference

Methods in netkit.inference with parameters of type Graph

void	InferenceMethodListener.iterate(Graph g, int[] unknown)
void	InferenceMethod.notifyListeners(Graph g, int[] unknown)

Uses of Graph in netkit.util

Fields in netkit.util declared as Graph

Graph

GraphMetrics.graph

Methods in netkit.util that return Graph

Graph	ModularityClusterer.getGraph()
Graph	GraphView.getGraph()

Constructors in netkit.util with parameters of type Graph

GraphMetrics(Graph g)
Compute metrics over all nodes in the graph

GraphMetrics(Graph g, java.lang.String nodeType)
Compute certain metrics only over nodes of the given node type

GraphView(Graph graph)

ModularityClusterer(Graph g)