def default_parameter(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"step": 5,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_unsupervised_node_classification(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"task": "unsupervised_node_classification",
"model": "spectral",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def default_parameter():
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 20,
"negative": 5,
"batch_size": 1000,
"alpha": 0.025,
"order": 3,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def default_parameter():
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 40,
"window_size": 5,
"worker": 10,
"iteration": 10,
"p": 1.0,
"q": 1.0,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_unsupervised_node_classification(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"window_size": 5,
"rank": 256,
"negative": 1,
"is_large": False,
"task": "unsupervised_node_classification",
"model": "netmf",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_multiplex_node_classification(dataset):
args = {
"hidden_size": 128,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 40,
"negative": 5,
"batch_size": 1000,
"alpha": 0.025,
"task": "multiplex_node_classification",
"model": "pte",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_multiplex_node_classification(dataset):
args = {
"hidden_size": 128,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 40,
"window_size": 5,
"worker": 10,
"iteration": 10,
"schema": "No",
"task": "multiplex_node_classification",
"model": "metapath2vec",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_unsupervised_node_classification(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 40,
"window_size": 5,
"worker": 10,
"iteration": 10,
"task": "unsupervised_node_classification",
"model": "deepwalk",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_unsupervised_node_classification(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"walk_length": 80,
"walk_num": 20,
"negative": 5,
"batch_size": 1000,
"alpha": 0.025,
"order": 3,
"task": "unsupervised_node_classification",
"model": "line",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_unsupervised_node_classification(dataset):
args = {
"hidden_size": 128,
"num_shuffle": 5,
"cpu": True,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"lr": 0.001,
"max_epoch": 500,
"hidden_size1": 1000,
"hidden_size2": 128,
"noise": 0.2,
"alpha": 0.1,
"step": 10,
"task": "unsupervised_node_classification",
"model": "dngr",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_multiplex_node_classification(dataset):
cpu = not torch.cuda.is_available()
args = {
"hidden_size": 128,
"cpu": cpu,
"enhance": None,
"save_dir": ".",
"seed": [0, 1, 2],
"lr": 0.025,
"walk_length": 80,
"walk_num": 40,
"batch_size": 1000,
"hop": 2,
"negative": 5,
"epochs": 1,
"task": "multiplex_node_classification",
"model": "hin2vec",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_node_classification(dataset,
missing_rate=0,
num_layers=40):
cpu = not torch.cuda.is_available()
args = {
"lr": 0.005,
"weight_decay": 5e-4,
"max_epoch": 1000,
"patience": 1000,
"cpu": cpu,
"device_id": [0],
"seed": [0, 1, 2, 3, 4],
"missing_rate": missing_rate,
"norm_mode": "PN",
"norm_scale": 10,
"dropout": 0.6,
"num_layers": num_layers,
"task": "node_classification",
"model": "sgcpn",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)
def build_default_args_for_multiplex_link_prediction(dataset):
cpu = not torch.cuda.is_available()
args = {
"hidden_size": 200,
"cpu": cpu,
"eval_type": "all",
"seed": [0, 1, 2],
"walk_length": 10,
"walk_num": 10,
"window_size": 5,
"worker": 10,
"epoch": 20,
"batch_size": 256,
"edge_dim": 10,
"att_dim": 20,
"negative_samples": 5,
"neighbor_samples": 10,
"schema": None,
"task": "multiplex_link_prediction",
"model": "gatne",
"dataset": dataset,
}
args = get_extra_args(args)
return build_args_from_dict(args)