Hi, everyone!
I met a problem when I'm learning how to use graph-tool. I read the paper,
network reconstruction and community detection from dynamics, and I am
trying to achieve the same result. When I followed the same settings for
real networks with synthetic dynamics, their similarities were just about
0.2. I have a question about how to control the number of infection events
per node,a, for the first model and the number of micro-state, M, for the
second model. The whole process is shown as following.
import graph_tool.all as gt
from matplotlib import cm
g = gt.collection.konect_data["openflights"] ## airport network with SIS
dynamics
gt.remove_parallel_edges(g)
g = gt.extract_largest_component(g, prune=False)
#simulation of an empirical dynamic model
# The algorithm accepts multiple independent time-series for the
# reconstruction. We will generate 100 SIS cascades starting from a
# random node each time, and uniform infection probability beta=0.2.
ss = []
for i in range(100):
si_state = gt.SISState(g, beta=.2)
s = [si_state.get_state().copy()]
for j in range(10):
si_state.iterate_sync()
s.append(si_state.get_state().copy())
# Each time series should be represented as a single vector-valued
# vertex property map with the states for each note at each time.
s = gt.group_vector_property(s)
ss.append(s)
# Prepare the initial state of the reconstruction as an empty graph
u = g.copy()
u.clear_edges()
ss = [u.own_property(s) for s in ss] # time series properties need to be
'owned' by graph u
# Create reconstruction state
rstate = gt.EpidemicsBlockState(u, s=ss, beta = None, r=1e-6,
global_beta=.2,
state_args=dict(B=20), nested=False,
aE=g.num_edges())
# Now we collect the marginals for exactly 10,000 sweeps, at
# intervals of 10 sweeps:
gm = None
bm = None
betas = []
def collect_marginals(s):
global gm, bm
gm = s.collect_marginal(gm)
b = gt.perfect_prop_hash([s.bstate.b])[0]
bm = s.bstate.collect_vertex_marginals(bm, b=b)
betas.append(s.params["global_beta"])
gt.mcmc_equilibrate(rstate, force_niter=1000, mcmc_args=dict(niter=10,
xstep=0),
callback=collect_marginals)
print("Posterior similarity: ", gt.similarity(g, gm, g.new_ep("double", 1),
gm.ep.eprob))
print("Inferred infection probability: %g ± %g" % (mean(betas), std(betas)))
##
g = gt.GraphView(gt.collection.konect_data["maayan-foodweb"],
directed=True)##a food web network with Ising dynamic
gt.remove_parallel_edges(g)
# The algorithm accepts multiple independent time-series for the
# reconstruction. We will generate 1000 Ising cascades starting from a
# random node each time, and the uniform inverse temperature beta=0.2.
ss = []
for i in range(1000):
si_state = gt.IsingGlauberState(g, beta=.1)
s = [si_state.get_state().copy()]
si_state.iterate_async(niter=1000)
s.append(si_state.get_state().copy())
# Each time series should be represented as a single vector-valued
# vertex property map with the states for each note at each time.
s = gt.group_vector_property(s)
ss.append(s)
u = g.copy()
u.clear_edges()
ss = [u.own_property(s) for s in ss]
rstate = gt.PseudoIsingBlockState(g,s=ss,beta=0.1,state_args=dict(B=1),
nested=False, aE=g.num_edges())
gm = None
bm = None
betas = []
def collect_marginals(s):
global gm, bm
gm = s.collect_marginal(gm)
b = gt.perfect_prop_hash([s.bstate.b])[0]
bm = s.bstate.collect_vertex_marginals(bm, b=b)
betas.append(s.params["beta"])
gt.mcmc_equilibrate(rstate, force_niter=1000, mcmc_args=dict(niter=10,
xstep=0),
callback=collect_marginals)
print("Posterior similarity: ", gt.similarity(g, gm, g.new_ep("double", 1),
gm.ep.eprob))
print("Inversed temperature: %g ± %g" % (mean(betas), std(betas)))
Moreover, I also wonder how to do a nested version for the same network.
Please let me know if you need more information on the question. otherwise,
I hope to hear how this can be achieved using graph-tool?
Thanks,
Gege Hou
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