def test_config(self):
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_initial_configuration("Infected", [1, 2, 3])
config.add_node_set_configuration("partial", {1: 1, 2: 2})
try:
model.set_initial_status(config)
except:
pass
if isinstance(g, nx.Graph):
edges = list(g.edges)
nodes = list(g.nodes)
else:
edges = [(g.vs[e.tuple[0]]['name'], g.vs[e.tuple[1]]['name'])
for e in g.es]
nodes = g.vs['name']
config.add_edge_set_configuration("partial",
{e: 1
for e in edges[:10]})
try:
model.set_initial_status(config)
except:
pass
config.add_node_set_configuration("partial", {n: 1 for n in nodes})
config.add_edge_set_configuration("partial", {e: 1 for e in edges})
model.set_initial_status(config)
for g in get_graph():
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("percentage_infected", 0.2)
try:
model.set_initial_status(config)
except:
pass
for g in get_graph(True):
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
def test_config(self):
g = nx.erdos_renyi_graph(99, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_initial_configuration("Infected", [1, 2, 3])
config.add_node_set_configuration("partial", {1: 1, 2: 2})
try:
model.set_initial_status(config)
except:
pass
config.add_edge_set_configuration("partial",
{e: 1
for e in list(g.edges)[:10]})
try:
model.set_initial_status(config)
except:
pass
config.add_node_set_configuration("partial", {n: 1 for n in g.nodes})
config.add_edge_set_configuration("partial", {e: 1 for e in g.edges})
model.set_initial_status(config)
g = nx.complete_graph(100)
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("fraction_infected", 0.2)
try:
model.set_initial_status(config)
except:
pass
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
def model (net, frac_inf, threshold, iter):
# Defines the model type, specification and implementation.
# The function uses the network input in net as the main input
# for the threshold model. Then the rest of parameters are
# specified in the model configuration:
# frac_inf: Initial fraction of infected nodes.
# threshold: Fraction of active neighbours necessary to change
# a node's state to active.
# iter: Number of iterations to run from the dynamical process.
# Model impolementation
model = ep.ThresholdModel(net)
# Model Configuration: initial conditions
config = mc.Configuration()
config.add_model_parameter('fraction_infected', frac_inf)
for i in net.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(iter)
trends = model.build_trends(iterations)
return [model, iterations, trends]
def tipping_model(self):
'''
Configuration and execution of the Tipping Model algorithm, iteration is performed by iteration.
Model execution is stopped when nodes are no longer influenced.
'''
model = ids.ThresholdModel(self.__year_graph)
config = mc.Configuration()
config.add_model_initial_configuration("Infected", self.__seed)
for n in self.__year_graph.nodes():
threshold = self.tipping_threshold(n)
config.add_node_configuration('threshold', n, threshold)
model.set_initial_status(config)
self.__influenced = dict()
while True:
iteration = model.iteration()
if len(iteration.get('status')) == 0:
break
self.__influenced['iteration {0}'.format(
iteration.get('iteration'))] = []
for k in iteration.get('status'):
if iteration.get('status')[k] == 1:
self.__influenced['iteration {0}'.format(
iteration.get('iteration'))].append(k)
def simulate_cascade(graph_path, threshold):
g = nx.read_weighted_edgelist(graph_path, delimiter=";")
model = ep.ThresholdModel(g)
config = mc.Configuration()
seed_nodes = find_seeds(g)
config.add_model_initial_configuration(
"Infected", seed_nodes) # only seed nodes start as infected
for i in g.nodes():
config.add_node_configuration(
'threshold', i,
threshold) # set threshold to be the same for all nodes
model.set_initial_status(config)
iterations = model.iteration_bunch(50) # execute 50 iterations
return iterations
def test_threshold_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
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_threshold_model(self):
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
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 watts_model_ba_network(m, threshold, fraction_infected, iteration_num):
# change these two parameters
# num_neighbors = 1
# threshold = 0.5
# fraction_infected = 0.1
# iteration_num = 30
# Network topology
#g = nx.erdos_renyi_graph(1000, 0.1)
g = nx.barabasi_albert_graph(1000, m)
# Model selection
model = ep.ThresholdModel(g)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('fraction_infected', fraction_infected)
# Setting node parameters
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(iteration_num)
return model, iterations
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)
g = nx.DiGraph()
with open('twitter_graph.txt', 'r') as f:
for row in f:
row = row.rstrip()
if not row:
continue
ls = row.split()
source = ls[0]
to = ls[1]
score = float(ls[2])
g.add_edge(source, to, weight=score)
# Model selection
model = ep.ThresholdModel(g)
config = mc.Configuration()
infected_nodes = ['70775228', '17845620', '512355630', '14191945', '211955846', '121858259', '54981205', '26143624', '20050047', '20801235']
config.add_model_initial_configuration("Infected", infected_nodes)
# Setting the edge parameters
threshold = 0.5
for i in g.nodes():
if i not in node_scores:
threshold = random.uniform(0, 1)
else:
threshold = node_scores[i]
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
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)
import matplotlib.pyplot as plt
import csv
flight_data_col = ['Source', 'Destination']
flight_data_df = pd.read_csv('flight_data.csv',
names=flight_data_col,
skiprows=[0])
flight_data_df['Source'] = flight_data_df['Source'].astype("string")
flight_data_df['Destination'] = flight_data_df['Destination'].astype("string")
original_data = nx.from_pandas_edgelist(flight_data_df,
source='Source',
target='Destination',
create_using=nx.DiGraph())
model = ep.ThresholdModel(original_data)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.10)
threshold = 0.25
for i in original_data.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
viz = DiffusionTrend(model, trends)
