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 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)
