EoN.EBCM_discrete_from_graph(G, p, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=100, return_full_data=False)[source]

Takes a given graph, finds the degree distribution (from which it gets psi), assumes a constant proportion of the population is infected at time 0, and then uses the discrete EBCM model.

G Networkx Graph
the contact network
p number
per edge transmission probability
initial infecteds node or iterable of nodes (default None)
if a single node, then this node is initially infected if an iterable, then whole set is initially infected if None, then choose randomly based on rho. If rho is also None, a random single node is chosen. If both initial_infecteds and rho are assigned, then there is an error.
initial_recovereds iterable of nodes (default None)
this whole collection is made recovered. Currently there is no test for consistency with initial_infecteds. Understood that everyone who isn’t infected or recovered initially is initially susceptible.
rho float between 0 and 1 (default None)
the fraction to be randomly infected at time 0 If None, then rho=1/N is used where N = G.order()
tmax number
maximum time
return_full_data boolean
if False,
return t, S, I, R and if True return t, S, I, R, and theta
if return_full_data == False:
returns t, S, I, R, all numpy arrays
if …== True
returns t, S, I, R and theta, all numpy arrays