edu.uci.ics.jung.utils

Class TestGraphs

java.lang.Object

edu.uci.ics.jung.utils.TestGraphs

public class TestGraphs
extends java.lang.Object

Generates a series of potentially useful test graphs.

Author:

danyelf

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String[][]	pairs A series of pairs that may be useful for generating graphs.

Constructor Summary

Constructors

Constructor and Description
TestGraphs()

Method Summary

Methods

Modifier and Type	Method and Description
static Graph	createChainPlusIsolates(int chain_length, int isolate_count) Returns a graph consisting of a chain of vertex_count - 1 vertices plus one isolated vertex.
static Graph	createDirectedAcyclicGraph(int layers, int maxNodesPerLayer, double linkprob) Creates a sample directed acyclic graph by generating several "layers", and connecting nodes (randomly) to nodes in earlier (but never later) layers.
static AbstractSparseGraph	createTestGraph(boolean isDirected) Creates a small sample graph that can be used for testing purposes.
static Graph	generateMixedRandomGraph(NumberEdgeValue edge_weight, int num_vertices) Equivalent to generateMixedRandomGraph(edge_weight, num_vertices, true).
static Graph	generateMixedRandomGraph(NumberEdgeValue edge_weights, int num_vertices, boolean parallel) Returns a random mixed-mode graph.
static Graph	getDemoGraph() Returns a bigger test graph with a clique, several components, and other parts.
static Graph	getOneComponentGraph() Returns a bigger, undirected test graph with a just one component.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

pairs

public static java.lang.String[][] pairs

A series of pairs that may be useful for generating graphs. The miniature graph consists of 8 edges, 10 nodes, and is formed of two connected components, one of 8 nodes, the other of 2.

Constructor Detail

TestGraphs

public TestGraphs()

Method Detail

createTestGraph

public static AbstractSparseGraph createTestGraph(boolean isDirected)

Creates a small sample graph that can be used for testing purposes. The graph is as described in the section on pairs. If isDirected, the graph is a DirectedSparseGraph, otherwise, it is an UndirectedSparseGraph.

Parameters:

isDirected: - Is the graph directed?

Returns:

a graph consisting of eight edges and ten nodes.

createChainPlusIsolates

public static Graph createChainPlusIsolates(int chain_length,
                            int isolate_count)

Returns a graph consisting of a chain of vertex_count - 1 vertices plus one isolated vertex.

createDirectedAcyclicGraph

public static Graph createDirectedAcyclicGraph(int layers,
                               int maxNodesPerLayer,
                               double linkprob)

Creates a sample directed acyclic graph by generating several "layers", and connecting nodes (randomly) to nodes in earlier (but never later) layers. Each layer has some random number of nodes in it 1 less than n less than maxNodesPerLayer.

Returns:

the created graph

getOneComponentGraph

public static Graph getOneComponentGraph()

Returns a bigger, undirected test graph with a just one component. This graph consists of a clique of ten edges, a partial clique (randomly generated, with edges of 0.6 probability), and one series of edges running from the first node to the last.

Returns:

the testgraph

getDemoGraph

public static Graph getDemoGraph()

Returns a bigger test graph with a clique, several components, and other parts.

Returns:

a demonstration graph of type UndirectedSparseGraph with 28 vertices.

generateMixedRandomGraph

public static Graph generateMixedRandomGraph(NumberEdgeValue edge_weight,
                             int num_vertices)

Equivalent to generateMixedRandomGraph(edge_weight, num_vertices, true).

generateMixedRandomGraph

public static Graph generateMixedRandomGraph(NumberEdgeValue edge_weights,
                             int num_vertices,
                             boolean parallel)

Returns a random mixed-mode graph. Starts with a randomly generated Barabasi-Albert (preferential attachment) generator (4 initial vertices, 3 edges added at each step, and num_vertices - 4 evolution steps). Then takes the resultant graph, replaces random undirected edges with directed edges, and assigns random weights to each edge.