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 testGenerate2(self): """ Make sure that the generated degree is less than or equal to the given degree """ numVertices = 10 for i in range(10): degSequence = numpy.random.randint(0, 3, numVertices) generator = ConfigModelGenerator(degSequence) graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) self.assertTrue((graph.outDegreeSequence() <= degSequence).all()) #We try to match an evolving degree sequence degSequence1 = numpy.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 4]) degSequence2 = numpy.array([2, 0, 3, 1, 2, 2, 2, 2, 3, 4]) degSequence3 = numpy.array([2, 1, 4, 1, 2, 2, 2, 2, 3, 6]) generator = ConfigModelGenerator(degSequence1) graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) self.assertTrue((degSequence1 >= graph.outDegreeSequence()).all()) deltaSequence = degSequence2 - graph.outDegreeSequence() generator = ConfigModelGenerator(deltaSequence) graph = generator.generate(graph, False) self.assertTrue((degSequence2 >= graph.outDegreeSequence()).all()) deltaSequence = degSequence3 - graph.outDegreeSequence() generator = ConfigModelGenerator(deltaSequence) graph = generator.generate(graph, False) self.assertTrue((degSequence3 >= graph.outDegreeSequence()).all())
def testGenerate2(self): """ Make sure that the generated degree is less than or equal to the given degree """ numVertices = 10 for i in range(10): degSequence = numpy.random.randint(0, 3, numVertices) generator = ConfigModelGenerator(degSequence) graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) self.assertTrue((graph.outDegreeSequence()<=degSequence).all()) #We try to match an evolving degree sequence degSequence1 = numpy.array([0,0,1,1,1,2,2,2,3, 4]) degSequence2 = numpy.array([2,0,3,1,2,2,2,2,3, 4]) degSequence3 = numpy.array([2,1,4,1,2,2,2,2,3, 6]) generator = ConfigModelGenerator(degSequence1) graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) self.assertTrue((degSequence1>= graph.outDegreeSequence()).all()) deltaSequence = degSequence2 - graph.outDegreeSequence() generator = ConfigModelGenerator(deltaSequence) graph = generator.generate(graph, False) self.assertTrue((degSequence2>= graph.outDegreeSequence()).all()) deltaSequence = degSequence3 - graph.outDegreeSequence() generator = ConfigModelGenerator(deltaSequence) graph = generator.generate(graph, False) self.assertTrue((degSequence3>= graph.outDegreeSequence()).all())
def testGenerate(self): k = 2 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) d = initialGraph.diameter() degreeSequence = initialGraph.outDegreeSequence() generator = KroneckerGenerator(initialGraph, k) graph = generator.generate() d2 = graph.diameter() degreeSequence2 = graph.outDegreeSequence() self.assertTrue((numpy.kron(degreeSequence, degreeSequence) == degreeSequence2).all()) self.assertTrue(graph.getNumVertices() == numVertices**k) self.assertTrue( graph.getNumDirEdges() == initialGraph.getNumDirEdges()**k) self.assertEquals(d, d2) #Try different k k = 3 generator.setK(k) graph = generator.generate() d3 = graph.diameter() degreeSequence3 = graph.outDegreeSequence() self.assertTrue((numpy.kron(degreeSequence, degreeSequence2) == degreeSequence3).all()) self.assertTrue(graph.getNumVertices() == numVertices**k) self.assertTrue( graph.getNumDirEdges() == initialGraph.getNumDirEdges()**k) self.assertEquals(d, d3) #Test the multinomial degree distribution logging.debug(degreeSequence) logging.debug(degreeSequence2) logging.debug(degreeSequence3)
def testGenerateGraph(self): k = 2 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) d = initialGraph.diameter() degreeSequence = initialGraph.outDegreeSequence() generator = StochasticKroneckerGenerator(initialGraph, k) graph = generator.generateGraph() d2 = graph.diameter() degreeSequence2 = graph.outDegreeSequence() self.assertTrue((numpy.kron(degreeSequence, degreeSequence) == degreeSequence2).all()) self.assertTrue(graph.getNumVertices() == numVertices**k) self.assertTrue(graph.getNumDirEdges() == initialGraph.getNumDirEdges()**k) self.assertEquals(d, d2) #Try different k k = 3 generator.setK(k) graph = generator.generateGraph() d3 = graph.diameter() degreeSequence3 = graph.outDegreeSequence() self.assertTrue((numpy.kron(degreeSequence, degreeSequence2) == degreeSequence3).all()) self.assertTrue(graph.getNumVertices() == numVertices**k) self.assertTrue(graph.getNumDirEdges() == initialGraph.getNumDirEdges()**k) self.assertEquals(d, d3) #Test the multinomial degree distribution logging.debug(degreeSequence) logging.debug(degreeSequence2) logging.debug(degreeSequence3)
def testGenerate(self): degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1]) generator = ConfigModelGenerator(degSequence) numVertices = 10 graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) tol = 3 self.assertTrue( numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol) degSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) generator.setOutDegSequence(degSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue( numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol) #Test using a non-empty graph degSequence = numpy.array([0, 0, 0, 2, 0, 0, 0, 1, 1, 0]) generator.setOutDegSequence(degSequence) oldDegSequence = graph.degreeSequence() self.assertRaises(ValueError, generator.generate, graph, True) graph = generator.generate(graph, False) diffSequence = graph.degreeSequence() - oldDegSequence self.assertTrue(numpy.linalg.norm(degSequence - diffSequence) < tol) #Test the case where we also have an in-degree sequence degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1]) inDegSequence = numpy.array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0]) generator = ConfigModelGenerator(degSequence, inDegSequence) graph = SparseGraph(GeneralVertexList(numVertices)) self.assertRaises(ValueError, generator.generate, graph) graph = SparseGraph(GeneralVertexList(numVertices), False) graph = generator.generate(graph) self.assertTrue( numpy.linalg.norm(degSequence - graph.outDegreeSequence()) < tol) self.assertTrue( numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol) outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1]) generator.setOutDegSequence(outDegSequence) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue( numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol) self.assertTrue( numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol) #In the case that the in-degree sequence sum larger than that of the out-degree it is #not satisfied, but the out-degree should be. inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 5, 6]) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue( numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol) #Now try the other way around generator.setOutDegSequence(inDegSequence) generator.setInDegSequence(outDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue( numpy.linalg.norm(outDegSequence - graph.inDegreeSequence()) < tol) #Test growing graph outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1]) generator.setOutDegSequence(outDegSequence) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) newOutDegreeSequence = numpy.array([2, 1, 3, 5, 2, 1, 4, 0, 0, 1]) newInDegreeSequence = numpy.array([2, 3, 2, 2, 3, 1, 2, 1, 2, 1]) diffOutSequence = newOutDegreeSequence - graph.outDegreeSequence() diffInSequence = newInDegreeSequence - graph.inDegreeSequence() generator.setOutDegSequence(diffOutSequence) generator.setInDegSequence(diffInSequence) graph = generator.generate(graph, False) self.assertTrue( numpy.linalg.norm(newOutDegreeSequence - graph.outDegreeSequence()) < tol) self.assertTrue( numpy.linalg.norm(newInDegreeSequence - graph.inDegreeSequence()) < tol)
def testGenerate(self): degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1]) generator = ConfigModelGenerator(degSequence) numVertices = 10 graph = SparseGraph(GeneralVertexList(numVertices)) graph = generator.generate(graph) tol = 3 self.assertTrue(numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol) degSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) generator.setOutDegSequence(degSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue(numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol) #Test using a non-empty graph degSequence = numpy.array([0, 0, 0, 2, 0, 0, 0, 1, 1, 0]) generator.setOutDegSequence(degSequence) oldDegSequence = graph.degreeSequence() self.assertRaises(ValueError, generator.generate, graph, True) graph = generator.generate(graph, False) diffSequence = graph.degreeSequence() - oldDegSequence self.assertTrue(numpy.linalg.norm(degSequence - diffSequence) < tol) #Test the case where we also have an in-degree sequence degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1]) inDegSequence = numpy.array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0]) generator = ConfigModelGenerator(degSequence, inDegSequence) graph = SparseGraph(GeneralVertexList(numVertices)) self.assertRaises(ValueError, generator.generate, graph) graph = SparseGraph(GeneralVertexList(numVertices), False) graph = generator.generate(graph) self.assertTrue(numpy.linalg.norm(degSequence - graph.outDegreeSequence()) < tol) self.assertTrue(numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol) outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1]) generator.setOutDegSequence(outDegSequence) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue(numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol) self.assertTrue(numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol) #In the case that the in-degree sequence sum larger than that of the out-degree it is #not satisfied, but the out-degree should be. inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 5, 6]) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue(numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol) #Now try the other way around generator.setOutDegSequence(inDegSequence) generator.setInDegSequence(outDegSequence) graph.removeAllEdges() graph = generator.generate(graph) self.assertTrue(numpy.linalg.norm(outDegSequence - graph.inDegreeSequence()) < tol) #Test growing graph outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1]) inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1]) generator.setOutDegSequence(outDegSequence) generator.setInDegSequence(inDegSequence) graph.removeAllEdges() graph = generator.generate(graph) newOutDegreeSequence = numpy.array([2, 1, 3, 5, 2, 1, 4, 0, 0, 1]) newInDegreeSequence = numpy.array([2, 3, 2, 2, 3, 1, 2, 1, 2, 1]) diffOutSequence = newOutDegreeSequence - graph.outDegreeSequence() diffInSequence = newInDegreeSequence - graph.inDegreeSequence() generator.setOutDegSequence(diffOutSequence) generator.setInDegSequence(diffInSequence) graph = generator.generate(graph, False) self.assertTrue(numpy.linalg.norm(newOutDegreeSequence - graph.outDegreeSequence()) < tol) self.assertTrue(numpy.linalg.norm(newInDegreeSequence - graph.inDegreeSequence()) < tol)