subgraphIndices = subgraphIndices[0:100]
graph = graph.subgraph(subgraphIndices)
d = 0.5
#Rewrite edges to have a decay rate of d
edges = graph.getAllEdges()
for m in range(edges.shape[0]):
graph.addEdge(edges[m, 0], edges[m, 1], d)
logging.info("Computing max product paths")
#Make sure the diagonal entrices have information 1
P = graph.maxProductPaths()
P = P + numpy.eye(numVertices)
u = numpy.random.rand(numVertices)
L = 0.1*numpy.sum(u)
logging.info("Computing max budgeted influence with branch and bound method")
influence2 = BABInfluence()
inds2 = influence2.maxBudgetedInfluence(P, u, L)
logging.info("Best activation: " + str(numpy.sum(numpy.max(P[inds2, :], 0))))
logging.info("Cost: " + str(numpy.sum(u[inds2, :], 0)) + "\n")
logging.info("Computing max budgeted influence with greedy method")
influence = GreedyInfluence()
inds = influence.maxBudgetedInfluence(P, u, L)
logging.info("Best activation: " + str(numpy.sum(numpy.max(P[inds, :], 0))))
logging.info("Cost: " + str(numpy.sum(u[inds, :], 0)) + "\n\n")
#This is really slow even with 100 vertices - need a better method to cull
