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)