file_address = r"../Data/PB.gml"
if data_set_name == 'bio-CE-GT':
file_address = r"../Data/bio-CE-GT.gml"
if data_set_name == 'hamster':
file_address = r"../Data/hamster.gml"
if data_set_name == 'USAir':
file_address = r"../Data/USAir.gml"
if data_set_name == 'Yeast':
file_address = r"../Data/Yeast.gml"
if torch.cuda.is_available():
GPU = True
else:
GPU = False
G, A, nodes, all_neighbors, As = process_gml_file(file_address)
train_loader, test_loader, A_test, weight = get_data_loader(
A, radio=radio, batch_size=batchSize, sample_method=sample_method, GPU=GPU)
if module_name == 'DeepWalk':
module = Deep_Walk(G=G,
A=A_test,
walk_length=walk_length,
embed_size=embed_size,
window_size=window_size,
workers=workers)
if module_name == 'Node2Vec':
module = Node2vec(G=G,
A=A_test,
walk_length=walk_length,
p=p,
q=q,
import torch
from torch import nn
from torch.nn import Parameter
import numpy
from torch.utils import data as Data
from Tools import process_gml_file
import torch.optim as optim
G, A, nodes, all_neighbors, As = process_gml_file(
r"C:\Users\mihao\Desktop\米昊的东西\input.gml")
def getDataLoader(A, radio):
data = []
label = []
N = len(A)
for i in range(N):
for j in range(N):
if i != j:
data.append([i, j])
label.append(A[i][j])
train_data = torch.tensor(data[:int(N * (N - 1) * radio)])
train_label = torch.tensor(label[:int(N * (N - 1) * radio)])
train_dataset = Data.TensorDataset(train_data, train_label)
train_loader = Data.DataLoader(
dataset=train_dataset,
batch_size=16,
shuffle=True,
)