ErdosRenyiNet

class ErdosRenyiNet(pars=None, key_dict=None, **kwargs)

Bases: DynamicNetwork

In the Erdos-Renyi network, every possible edge has a probability, p, of being created on each time step.

The degree of each node will have a binomial distribution, considering each of the N-1 possible edges connection this node to the others will be created with probability p.

Please be careful with the dur parameter. When set to 0, new edges will be created on each time step. If positive, edges will persist for dur years. Note that the existence of edges from previous time steps will not prevent or otherwise alter the creation of new edges on each time step, edges will accumulate over time.

Attributes

beta	Relative transmission on each network edge
members	Return sorted array of all members
now	Shortcut to self.t.now()
p1	The first half of a network edge (person 1)
p2	The second half of a network edge (person 2)
states	Return a flat list of all states
statesdict	Return a flat dictionary (objdict) of all states
ti	Get the current module timestep
timevec	Shortcut to self.t.timevec

Methods

add_pairs()

Generate contacts