def experiment_redundancy(graph):
params = {
'runs': 10,
'steps': 100,
'seed': 1,
'l': 0.8,
'r': 0.2,
'c': int(0.1 * len(graph)),
'k_a': 5,
'attack': 'id_node',
'attack_approx': None, # int(0.1 * len(graph)),
'k_d': 0,
'defense': None,
'robust_measure': 'largest_connected_component',
'plot_transition': False,
'gif_animation': False,
'edge_style': None,
'node_style': 'spectral',
'fa_iter': 2000,
}
results = defaultdict(list)
redundancy = np.arange(0, 0.5, .1)
for idx, r in enumerate(redundancy):
params['r'] = r
if idx == 2:
params['plot_transition'] = True
params['gif_animation'] = False
params['gif_snaps'] = False
else:
params['plot_transition'] = False
params['gif_animation'] = False
params['gif_snaps'] = False
cf = Cascading(graph, **params)
results[r] = cf.run_simulation()
plot_results(graph, params, results, xlabel='Steps', line_label='Redundancy', experiment='redundancy')
def test_cascading():
params = {
'runs': 10,
'steps': 30,
'l': 0.8,
'r': 0.5,
'capacity_approx': np.inf,
'k_a': 4,
'attack': 'rnd_node',
'k_d': 0,
'defense': None,
'robust_measure': 'largest_connected_component',
'seed': 1,
'plot_transition': False,
'gif_animation': False
}
graph = karate()
cf = Cascading(graph, **params)
attacked = cf.run_simulation()
params['k_a'] = 0
params['attack'] = None
cf = Cascading(graph, **params)
baseline = cf.run_simulation()
params['k_a'] = 4
params['attack'] = 'rnd_node'
params['k_d'] = 4
params['defense'] = 'pr_node'
cf = Cascading(graph, **params)
defended = cf.run_simulation()
assert sum(attacked) <= sum(defended) <= sum(baseline)
def experiment_attack(graph):
params = {
'runs': 10,
'steps': 100,
'seed': 1,
'l': 0.8,
'r': 0.4,
'c': int(0.1 * len(graph)),
'k_a': 5,
'attack': 'rnd_node',
'attack_approx': None, # int(0.1 * len(graph)),
'k_d': 0,
'defense': None,
'robust_measure': 'largest_connected_component',
'plot_transition': False,
'gif_animation': False,
'edge_style': None,
'node_style': 'spectral',
'fa_iter': 2000,
}
# rnd_node attack
results = defaultdict(list)
attack_strength = np.arange(2, 11, 2)
for idx, k_a in enumerate(attack_strength):
params['k_a'] = k_a
if idx == 2:
params['plot_transition'] = False
params['gif_animation'] = False
else:
params['plot_transition'] = False
params['gif_animation'] = False
cf = Cascading(graph, **params)
results[k_a] = cf.run_simulation()
plot_results(graph,
params,
results,
xlabel='Steps',
line_label='k_a',
experiment='rnd_node_attack')
# targeted attack
params['attack'] = 'id_node'
results = defaultdict(list)
for idx, k_a in enumerate(attack_strength):
params['k_a'] = k_a
if idx == 2:
params['plot_transition'] = False
params['gif_animation'] = False
else:
params['plot_transition'] = False
params['gif_animation'] = False
cf = Cascading(graph, **params)
results[k_a] = cf.run_simulation()
plot_results(graph,
params,
results,
xlabel='Steps',
line_label='k_a',
experiment='id_node_attack')
def experiment_defense(graph):
params = {
'runs': 10,
'steps': 100,
'seed': 1,
'l': 0.8,
'r': 0.2,
'c': int(0.1 * len(graph)),
'k_a': 5,
'attack': 'id_node',
'attack_approx': None, # int(0.1 * len(graph)),
'k_d': 0,
'defense': 'add_edge_preferential',
'robust_measure': 'largest_connected_component',
'plot_transition': False,
'gif_animation': False,
'edge_style': None,
'node_style': 'spectral',
'fa_iter': 2000,
}
# edge defense
results = defaultdict(list)
defense_strength = np.arange(10, 51, 10)
for idx, k_d in enumerate(defense_strength):
params['k_d'] = k_d
if idx == 2:
params['plot_transition'] = False
params['gif_animation'] = False
else:
params['plot_transition'] = False
params['gif_animation'] = False
cf = Cascading(graph, **params)
results[k_d] = cf.run_simulation()
plot_results(graph,
params,
results,
xlabel='Steps',
line_label='k_d',
experiment='add_edge_pref')
# node defense
params['defense'] = 'pr_node'
defense_strength = np.arange(1, 10, 2)
results = defaultdict(list)
for idx, k_d in enumerate(defense_strength):
params['k_d'] = k_d
if idx == 2:
params['plot_transition'] = False
params['gif_animation'] = False
else:
params['plot_transition'] = False
params['gif_animation'] = False
cf = Cascading(graph, **params)
results[k_d] = cf.run_simulation()
plot_results(graph,
params,
results,
xlabel='Steps',
line_label='k_d',
experiment='add_node_pr')