Diffusion

class graph_tiger.diffusion.Diffusion(graph, model='SIS', runs=10, steps=5000, b=0.00208, d=0.01, c=1, **kwargs)

Bases: Simulation

Simulates the propagation of a virus using either the SIS or SIR model [15].

Parameters

  • graph – contact network

  • model – a string to set the model type (i.e., SIS or SIR)

  • runs – an integer number of times to run the simulation

  • steps – an integer number of steps to run a single simulation

  • b – float representing birth rate of virus (probability of transmitting disease to each neighbor)

  • d – float representing death rate of virus (probability of each infected node healing)

  • c – fraction of initially infected nodes

  • **kwargs –

    see parent class Simulation for additional options

get_effective_strength()

Gets the effective string of the virus. This is a factor of the spectral radius (first eigenvalue) of graph, the virus birth rate ‘b’ and the virus death rate ‘d’

Returns

a float for virus effective strength

reset_simulation()

Resets the simulation between each run

run_single_sim()

The initially infected nodes are chosen uniformly at random. At each time step, every susceptible (i.e., non-infected) node has a probability ‘b’ of being infected by neighboring infected nodes. Every infected node has a probability ‘d’ of being cured and becoming susceptible again (or recovered for SIR model).

track_simulation(step)

Keeps track of important simulation information at each step of the simulation

Parameters

step – current simulation iteration

graph_tiger.diffusion.main()