def test_kertesz_model_predefined_blocked(self):
for g in get_graph(True):
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked",
predefined_blocked)
config.add_model_parameter('percentage_infected', 0.1)
threshold = 0.2
if isinstance(g, nx.Graph):
nodes = g.nodes
else:
nodes = g.vs['name']
for i in nodes:
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [
x for x, v in future.utils.iteritems(iteration['status'])
if v == -1
]
self.assertEqual(blocked, predefined_blocked)
def test_kertesz_model(self):
g = nx.complete_graph(100)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
threshold = 0.2
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_kertesz_model_predefined_blocked(self):
g = nx.complete_graph(100)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked", predefined_blocked)
config.add_model_parameter('fraction_infected', 0.1)
threshold = 0.2
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [
x for x, v in future.utils.iteritems(iteration['status'])
if v == -1
]
self.assertEqual(blocked, predefined_blocked)
def test_kertesz_model(self):
for g in get_graph():
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
threshold = 0.2
if isinstance(g, nx.Graph):
nodes = g.nodes
else:
nodes = g.vs['name']
for i in nodes:
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_optional_parameters(self):
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
if isinstance(g, nx.Graph):
config.add_node_set_configuration("test", {n: 1 for n in g.nodes})
config.add_edge_set_configuration("etest", {e: 1 for e in g.edges})
else:
config.add_node_set_configuration("test", {n: 1 for n in g.vs['name']})
edges = [(g.vs[e.tuple[0]]['name'], g.vs[e.tuple[1]]['name']) for e in g.es]
config.add_edge_set_configuration("etest", {e: 1 for e in edges})
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked", predefined_blocked)
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [x for x, v in future.utils.iteritems(iteration["status"]) if v == -1]
self.assertEqual(blocked, predefined_blocked)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
def test_optional_parameters(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
config.add_node_set_configuration("test", {n: 1 for n in g.nodes()})
config.add_edge_set_configuration("etest", {e: 1 for e in g.edges()})
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked", predefined_blocked)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [
x for x, v in future.utils.iteritems(iteration["status"])
if v == -1
]
self.assertEqual(blocked, predefined_blocked)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
#
# The Kertesz Threshold model was introduced in 2015 by `Ruan` et al. [1] and it is an extension of the Watts threshold model [2].
#
# We set the initial infected as well blocked node sets equals to the 10% of the overall population, assign a threshold of 0.25 to all the nodes and impose an probability of spontaneous adoptions of 40%.
#
# - [1] Z. Ruan, G. In ̃iguez, M. Karsai, and J. Kertész, “Kinetics of social contagion,” Phys. Rev. Lett., vol. 115, p. 218702, Nov 2015.
# - [2] D.J. Watts, “A simple model of global cascades on random networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 9, pp. 5766–5771, 2002.
# In[18]:
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
th_model = ep.KerteszThresholdModel(g)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
# Setting node parameters
threshold = 0.25
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
th_model.set_initial_status(config)
# Simulation execution
