EoN.SIS_heterogeneous_meanfield_from_graph¶
-
EoN.
SIS_heterogeneous_meanfield_from_graph
(G, tau, gamma, initial_infecteds=None, rho=None, tmin=0, tmax=100, tcount=1001, return_full_data=False)[source]¶ Calls SIS_heterogeneous_meanfield after calculating Sk0, Ik0 based on the graph G and random fraction infected rho.
Arguments: - G networkx Graph
- the contact network
- tau positive float
- transmission rate
- gamma number
- recovery rate
- 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.
- 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()
- tmin number (default 0)
- minimum report time
- tmax number (default 100)
- maximum report time
- tcount integer (default 1001)
- number of reports
- return_full_data boolean
- tells whether to just return times, S, I, R or all calculated data.
Returns: - if return_full_data is True:
- times, S, I, Sk, Ik (the Xk are numpy 2D arrays)
- if return_full_data is False:
- times, S, I (all numpy arrays)
SAMPLE USE: import networkx as nx import EoN G = nx.configuration_model([1,2,3,4]*1000) tau = 1 gamma = 2 t, S, I = EoN.SIS_heterogeneous_meanfield_from_graph(G, tau, gamma, tmax = 15)