Dear professor Peixoto,
Thank you for reply!
The dynamic example in the document sets 100 initial infected points and
iterates for 10 times simultaneously. So the epidemic process is ongoing on
a network and time T belongs to [0,9]. Then the time series is copied to a
same but masked network. Am I correct? But I still wonder how to control the
number of infected events per node. I noted that infected nodes are randomly
selected.
Moreover, Should I set like this for the Ising model?
"
for i in range(1000):
si_state = gt.IsingGlauberState(g, beta=.02)
s = [si_state.get_state().copy()]
si_state.iterate_async()
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)
"
sincerely,
Gege Hou
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