def testGraphDisplay(self): try: import networkx import matplotlib except ImportError as error: logging.debug(error) return #Show numFeatures = 1 numVertices = 20 vList = VertexList(numVertices, numFeatures) graph = SparseGraph(vList) ell = 2 m = 2 generator = BarabasiAlbertGenerator(ell, m) graph = generator.generate(graph) logging.debug((graph.degreeDistribution())) nxGraph = graph.toNetworkXGraph() nodePositions = networkx.spring_layout(nxGraph) nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
def testDegreeDistribution(self): #We want to see how the degree distribution changes with kronecker powers numVertices = 3 numFeatures = 0 vList = VertexList(numVertices, numFeatures) initialGraph = SparseGraph(vList) initialGraph.addEdge(0, 1) initialGraph.addEdge(1, 2) for i in range(numVertices): initialGraph.addEdge(i, i) logging.debug((initialGraph.outDegreeSequence())) logging.debug((initialGraph.degreeDistribution())) k = 2 generator = KroneckerGenerator(initialGraph, k) graph = generator.generate() logging.debug((graph.outDegreeSequence())) logging.debug((graph.degreeDistribution())) k = 3 generator = KroneckerGenerator(initialGraph, k) graph = generator.generate() logging.debug((graph.degreeDistribution()))
def testDegreeDistribution(self): #We want to see how the degree distribution changes with kronecker powers numVertices = 3 numFeatures = 0 vList = VertexList(numVertices, numFeatures) initialGraph = SparseGraph(vList) initialGraph.addEdge(0, 1) initialGraph.addEdge(1, 2) for i in range(numVertices): initialGraph.addEdge(i, i) logging.debug((initialGraph.outDegreeSequence())) logging.debug((initialGraph.degreeDistribution())) k = 2 generator = StochasticKroneckerGenerator(initialGraph, k) graph = generator.generateGraph() logging.debug((graph.outDegreeSequence())) logging.debug((graph.degreeDistribution())) k = 3 generator = StochasticKroneckerGenerator(initialGraph, k) graph = generator.generateGraph() logging.debug((graph.degreeDistribution()))
def testDegreeDistribution(self): numFeatures = 0 numVertices = 100 vList = VertexList(numVertices, numFeatures) graph = SparseGraph(vList) alpha = 10.0 p = 0.01 dim = 2 generator = GeometricRandomGenerator(graph) graph = generator.generateGraph(alpha, p, dim) logging.debug((graph.degreeDistribution()))