def test_sis_model():
params = {
'model': 'SIS',
'b': 0.00208,
'd': 0.01,
'c': 1,
'runs': 10,
'steps': 5000,
'diffusion': 'max',
'method': 'add_edge_random',
'k': 15,
'seed': 1,
'plot_transition': False,
'gif_animation': False
}
graph = karate()
ds = Diffusion(graph, params)
increased_diffusion = ds.run_simulation()
params['diffusion'] = None
params['method'] = None
params['k'] = 0
ds = Diffusion(graph, params)
baseline_diffusion = ds.run_simulation()
params['diffusion'] = 'min'
params['method'] = 'ns_node'
params['k'] = 4
ds = Diffusion(graph, params)
decreased_diffusion = ds.run_simulation()
assert sum(decreased_diffusion) < sum(baseline_diffusion) < sum(increased_diffusion)
def run_epidemic_experiment(params):
graph = as_733().copy()
results = defaultdict(list)
b_list = np.arange(0, 0.005, 0.001) # transmission probability
for idx, b in enumerate(b_list):
params['b'] = b
if idx == 1:
params['plot_transition'] = True
params['gif_animation'] = True
params['gif_snaps'] = True
else:
params['plot_transition'] = False
params['gif_animation'] = False
params['gif_snaps'] = False
ds = Diffusion(graph, **params)
result = ds.run_simulation()
results[ds.get_effective_strength()] = result
plot_results(graph, params, results)
