Am 23.03.20 um 15:25 schrieb gege:
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?
In the example in the documentation the time series is copied to an empty graph, which will be the starting point of the reconstruction.
But I still wonder how to control the number of infected events per node. I noted that infected nodes are randomly selected.
This is not controlled explicitly; after you generate the time series you count the number of times each node flipped, and you average.
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) "
Since the Ising reconstruction expects uncorrelated samples, I think it's best to use only one "time series", i.e.
si_state = gt.IsingGlauberState(g, beta=.02) ss = [si_state.get_state().copy()]
for i in range(1000): si_state.iterate_async() ss.append(si_state.get_state().copy())
ss = gt.group_vector_property(ss)