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)
