if __name__ == '__main__':
# experimental setup
seed = 200494
random.seed(seed)
numpy.random.seed(seed)
graph_size = 5000
no_infected_nodes = round(0.01 * graph_size)
m = 20
graph = nx.barabasi_albert_graph(graph_size, m, seed)
# creating noisy graph
noisy_graph = NoisyGraph()
noisy_graph.add_edges_from(graph.edges, real=True)
# printing headers
print(
'fraction,graph_uncertainty,mean_uncertainty,std_dev_uncertainty,min_uncertainty,max_uncertainty,'
'number_contacts')
# generating 20 observations
for i in range(0, 101, 5):
# obtaining fraction
fraction = i / 100
# adding edges from missing_edges list
noisy_graph.add_missing_edges_per_node_ensuring_fraction(fraction)
'centrality_metric,mean_se_value,min_se_value,max_se_value')
# set of old_experiments for every graph size
for m in ms:
# generating original graph
graph = nx.barabasi_albert_graph(graph_size, m, seed)
# obtaining original centrality metrics
original_metrics = {
alg.__name__: alg(graph)
for alg in centrality_algorithms
}
# creating noisy graph
noisy_graph = NoisyGraph()
noisy_graph.add_edges_from(graph.edges, real=True)
# obtaining missing edges list
missing_edges = noisy_graph.missing_edges()
random.shuffle(missing_edges)
# starting counters
no_missing_edges = len(missing_edges)
start_index = 0
# generating 20 observations
for i in range(0, 101, 5):
# obtaining ending index
fraction = i / 100
end_index = round(no_missing_edges * fraction)
'centrality_metric,mean_se_value,min_se_value,max_se_value')
# model setup
m = 20
# set of old_experiments for every graph size
for graph_size in graph_sizes:
# generating original graph
graph = nx.barabasi_albert_graph(graph_size, m, seed)
# obtaining original centrality metrics
original_metrics = {alg.__name__: alg(graph) for alg in centrality_algorithms}
# creating noisy graph
noisy_graph = NoisyGraph()
noisy_graph.add_edges_from(graph.edges, real=True)
# generating 20 observations
for i in range(0, 101, 5):
# obtaining fraction
fraction = i / 100
# adding edges from missing_edges list
noisy_graph.add_missing_edges_per_node_ensuring_fraction(fraction)
# calculating uncertainty values
graph_uncertainty = noisy_graph.uncertainty()
mean_uncertainty, std_dev_uncertainty, min_uncertainty, max_uncertainty = noisy_graph.uncertainty_profile()
# disturbing graph