def create_and_run_model(args):
"""
Method to run the model.
:param args: Arguments object.
"""
graph = graph_reader(args.input)
model = LabelPropagator(graph, args)
model.do_a_series_of_propagations()
def create_and_run_model(args):
"""
Method to run the model.
:param args: Arguments object.
"""
graph = graph_reader(args.input)
result = content_reader(args.content)
kshell=value_kshell(args.kshell)
model = LabelPropagator(graph,args)
model.do_a_series_of_propagations()
def create_and_run_model(args):
"""
Function to read the graph, create an embedding and train it.
"""
graph = graph_reader(args.input)
if args.model == "GEMSECWithRegularization":
model = GEMSECWithRegularization(args, graph)
elif args.model == "GEMSEC":
model = GEMSEC(args, graph)
elif args.model == "DeepWalkWithRegularization":
model = DeepWalkWithRegularization(args, graph)
else:
model = DeepWalk(args, graph)
model.train()
def create_and_run_model(args):
"""
Function to read the graph, create an embedding and train it.
"""
graph = graph_reader(args.input)
if args.model == "GRAFCODEWithRegularization":
model = GRAFCODEWithRegularization(args, graph)
elif args.model == "GRAFCODE":
model = GRAFCODE(args, graph)
elif args.model == "GRAFWithRegularization":
model = GRAFWithRegularization(args, graph)
else:
model = GRAF(args, graph)
model.train()
def create_and_run_model(args):
graph = graph_reader(args.input)
model = LabelPropagator(graph, args)
model.do_a_series_of_propagations()