def testSimulateCascades(self):
#Test submodularity
n = 20
p = 0.1
graph = igraph.Graph.Erdos_Renyi(n, p)
numRuns = 1
influences = numpy.zeros(n)
activeVertexInds = set([0])
lastInfluence = MaxInfluence.simulateCascades(graph, activeVertexInds, numRuns)
for i in range(n):
activeVertexInds = set([0, i])
influences[i] = MaxInfluence.simulateCascades(graph, activeVertexInds, numRuns)
marginalGains = (influences - lastInfluence)
activeVertexInds = set([0, 1])
lastInfluence = MaxInfluence.simulateCascades(graph, activeVertexInds, numRuns)
for i in range(n):
activeVertexInds = set([0, 1, i])
influences[i] = MaxInfluence.simulateCascades(graph, activeVertexInds, numRuns)
marginalGains2 = (influences - lastInfluence)
def testGreedyMethod(self):
graph = igraph.Graph(directed=True)
graph.add_vertices(5)
graph.add_edges([(0,1), (0,2), (1, 3), (2,3), (2,4)])
k = 3
influenceList = MaxInfluence.greedyMethod(graph, k)
self.assertEquals(len(influenceList), k)
def testSimulateCascade(self):
graph = igraph.Graph(directed=True)
numVertices = 5
graph.add_vertices(numVertices)
graph.add_edges([(0,2), (1, 3), (2,4)])
activeVertexInds = set([0])
outputInds = MaxInfluence.simulateCascade(graph, activeVertexInds, p=1)
self.assertEquals(outputInds, set([0,2,4]))
outputInds = MaxInfluence.simulateCascade(graph, activeVertexInds, p=1)
self.assertEquals(outputInds, set([0, 2, 4]))
graph = igraph.Graph(directed=True)
numVertices = 5
graph.add_vertices(numVertices)
outputInds = MaxInfluence.simulateCascade(graph, activeVertexInds, p=1)
self.assertEquals(outputInds, set([0]))
def testGreedyMethod2(self):
graph = igraph.Graph()
graph.add_vertices(5)
graph.add_edges([(0,1), (0,2), (1, 3), (2,3), (2,4)])
k = 3
influenceList = MaxInfluence.greedyMethod2(graph, k)
self.assertEquals(len(influenceList), k)
#Now try some random graphs
numReps = 10
numRuns = 1
for i in range(numReps):
n = numpy.random.randint(10, 50)
p = numpy.random.rand()
graph = igraph.Graph.Erdos_Renyi(n, p)
k = numpy.random.randint(5, 10)
influenceList = MaxInfluence.greedyMethod2(graph, k, numRuns, p=1)
influenceList2 = MaxInfluence.greedyMethod(graph, k, numRuns, p=1)
self.assertEquals(influenceList, influenceList2)
#Test with p!=1
graph = igraph.Graph()
graph.add_vertices(8)
graph.add_edges([(0,1), (0,2), (0, 3), (0, 4), (1,6), (2,5), (5,7)])
k = 7
influenceList, influenceScores = MaxInfluence.greedyMethod2(graph, k, numRuns=1000, p=0.1, verbose=True)
influenceList2, influenceScores2 = MaxInfluence.greedyMethod(graph, k, numRuns=1000, verbose=True, p=0.1)
self.assertEquals(influenceList[0:3], influenceList2[0:3])
def testCelf(self):
graph = igraph.Graph(directed=True)
graph.add_vertices(5)
graph.add_edges([(0,1), (0,2), (1, 3), (2,3), (2,4)])
graph.es["p"] = numpy.array([0, 1, 1, 0, 1])
k = 3
influenceList = MaxInfluence.celf(graph, k)
self.assertEquals(set(influenceList), set([0,1,2]))
#2nd example
graph = igraph.Graph(directed=True)
graph.add_vertices(8)
graph.add_edges([(0,1), (0,2), (1, 3), (2,3), (2,4), (5,6), (6,7), (5, 4)])
k = 3
influenceList = MaxInfluence.celf(graph, k)
#self.assertEquals(influenceList, [5, 0, 1])
#Now try some random graphs
numReps = 10
numRuns = 1
for i in range(numReps):
print(i)
n = numpy.random.randint(10, 50)
p = numpy.random.rand()
graph = igraph.Graph.Erdos_Renyi(n, p)
k = numpy.random.randint(5, 10)
influenceList = MaxInfluence.celf(graph, k, numRuns, p=1)
influenceList2 = MaxInfluence.greedyMethod(graph, k, numRuns, p=1)
self.assertEquals(influenceList, influenceList2)
#Test with p!=1
graph = igraph.Graph()
graph.add_vertices(8)
graph.add_edges([(0,1), (0,2), (0, 3), (0, 4), (1,6), (2,5), (5,7)])
k = 7
influenceList, influenceScores = MaxInfluence.celf(graph, k, numRuns=1000, p=0.1, verbose=True)
influenceList2, influenceScores2 = MaxInfluence.greedyMethod(graph, k, numRuns=1000, verbose=True, p=0.1)
self.assertEquals(influenceList[0:3], influenceList2[0:3])
def testSimulateAllCascades(self):
n = 100
p = 0.01
graph = igraph.Graph.Erdos_Renyi(n, p)
numRuns = 10000
influences = numpy.zeros(n)
for i in range(numRuns):
influences += MaxInfluence.simulateAllCascades(graph, [], p=0.2)
influences /= numRuns
#Now compute influence via the other way
influences2 = numpy.zeros(n)
for i in range(n):
influences2[i] = MaxInfluence.simulateCascades(graph, set([i]), numRuns, p=0.2)
nptst.assert_array_almost_equal(influences, influences2, 1)
#Now test with p=1
influences = MaxInfluence.simulateAllCascades(graph, [], p=1.0)
influences2 = numpy.zeros(n)
for i in range(n):
influences2[i] = MaxInfluence.simulateCascades(graph, set([i]), 1, p=1.0)
nptst.assert_array_almost_equal(influences, influences2)
#Test where we have an active vertex
influences = MaxInfluence.simulateAllCascades(graph, [0, 10, 20], p=1.0)
influences2 = numpy.zeros(n)
for i in range(n):
influences2[i] = MaxInfluence.simulateCascades(graph, set([0, 10, 20, i]), 1, p=1.0)
nptst.assert_array_almost_equal(influences, influences2)
#Now test with p=0
influences = MaxInfluence.simulateAllCascades(graph, [], p=0.0)
influences2 = numpy.zeros(n)
for i in range(n):
influences2[i] = MaxInfluence.simulateCascades(graph, set([i]), 1, p=0.0)
nptst.assert_array_almost_equal(influences, influences2)
#See if we get more accurate results on a small graph
numRuns = 100000
graph = igraph.Graph()
graph.add_vertices(8)
graph.add_edges([(0,1), (0,2), (1, 3), (2,3), (2,4), (5,6), (6,7), (5, 4)])
influences = numpy.zeros(graph.vcount())
for i in range(numRuns):
influences += MaxInfluence.simulateAllCascades(graph, [], p=0.2)
influences /= numRuns
influences2 = numpy.zeros(graph.vcount())
for i in range(graph.vcount()):
influences2[i] = MaxInfluence.simulateCascades(graph, set([i]), numRuns, p=0.2)
nptst.assert_array_almost_equal(influences, influences2, 2)
#Test with some initial vertices
influences = numpy.zeros(graph.vcount())
for i in range(numRuns):
influences += MaxInfluence.simulateAllCascades(graph, [0], p=0.2)
influences /= numRuns
influences2 = numpy.zeros(graph.vcount())
for i in range(graph.vcount()):
influences2[i] = MaxInfluence.simulateCascades(graph, set([0, i]), numRuns, p=0.2)
print(influences, influences2)
nptst.assert_array_almost_equal(influences, influences2, 2)
influences = numpy.zeros(graph.vcount())
for i in range(numRuns):
influences += MaxInfluence.simulateAllCascades(graph, [0, 5], p=0.2)
influences /= numRuns
influences2 = numpy.zeros(graph.vcount())
for i in range(graph.vcount()):
influences2[i] = MaxInfluence.simulateCascades(graph, set([0, 5, i]), numRuns, p=0.2)
print(influences, influences2)
nptst.assert_array_almost_equal(influences, influences2, 2)
