def __init__(self,A, As, all_nodes_neighbors, N, d, layers, steps, delay, weight, GPU = False):
super(MihGNNEmbedding12WithJaccard, self).__init__()
self.original_A = torch.tensor(A, dtype=torch.float)
self.one_hop_matrix = torch.matmul(self.original_A, self.original_A)
self.A = Matrix_pre_handle(A, steps, delay)
self.A = torch.tensor(self.A, dtype = torch.float)
self.d = d
self.e = torch.tensor(math.e, dtype=torch.float)
self.layers = layers
self.As = As
self.weight = torch.tensor(weight, dtype = torch.float)
self.all_nodes_neighbors = all_nodes_neighbors
embedding_state = numpy.random.randn(N, d)
embedding_state = torch.tensor(data=embedding_state, dtype=torch.float)
self.aggregationModule = MihGNNAggregationModule2(A = self.A, As=As, all_nodes_neighbors = all_nodes_neighbors,
convolution_layers=layers, d=d,
embedding_states=embedding_state)
self.edge_generator_forward = nn.Sequential(
nn.Linear(d * 2, d * 2, bias=True),
nn.BatchNorm1d(num_features = d * 2),
nn.ReLU(),
nn.Linear(d * 2, d * 2, bias=True),
nn.BatchNorm1d(num_features=d * 2),
nn.ReLU()
)
# self.edge_generator_backward = nn.Sequential(
# nn.Linear(d * 2, d * 2, bias=True),
# nn.BatchNorm1d(num_features=d * 2),
# nn.ReLU()
# )
self.liner = LineNetwork(input_features=d * 2, output_features=2, hidden_features=d)
self.soft_max = nn.Softmax(dim = -1)
self.cross_entropy = nn.CrossEntropyLoss(weight = self.weight, reduction = 'sum')
def __init__(self,
A,
As,
all_nodes_neighbors,
N,
d,
layers,
steps,
delay,
GPU=False):
super(MihGNNEmbedding13, self).__init__()
self.A = Matrix_pre_handle(A, steps, delay)
self.A = torch.tensor(self.A, dtype=torch.float)
self.d = d
self.e = torch.tensor(math.e, dtype=torch.float)
self.layers = layers
self.As = As
self.all_nodes_neighbors = all_nodes_neighbors
embedding_state = numpy.random.randn(N, d)
embedding_state = torch.tensor(data=embedding_state, dtype=torch.float)
self.aggregationModule = MihGNNAggregationModule2(
A=self.A,
As=As,
all_nodes_neighbors=all_nodes_neighbors,
convolution_layers=layers,
d=d,
embedding_states=embedding_state)
self.lossFunction = MihOutputModule1(d=self.d, e=self.e, layers=layers)
def __init__(self,
A,
As,
all_nodes_neighbors,
N,
d,
layers,
steps,
delay,
GPU=False):
super(MihGNNEmbedding12WithTrainWeight, self).__init__()
self.A = Matrix_pre_handle(A, steps, delay)
self.A = torch.tensor(self.A, dtype=torch.float)
self.d = d
self.e = torch.tensor(math.e, dtype=torch.float)
self.layers = layers
self.As = As
self.weight = torch.tensor([1, 1], dtype=torch.float)
self.weight = Parameter(self.weight, requires_grad=True)
self.all_nodes_neighbors = all_nodes_neighbors
embedding_state = numpy.random.randn(N, d)
embedding_state = torch.tensor(data=embedding_state, dtype=torch.float)
self.aggregationModule = MihGNNAggregationModule2(
A=self.A,
As=As,
all_nodes_neighbors=all_nodes_neighbors,
convolution_layers=layers,
d=d,
embedding_states=embedding_state)
self.liner = LineNetwork(input_features=d * 2,
output_features=2,
hidden_features=d)
self.soft_max = nn.Softmax(dim=-1)
self.loss = nn.NLLLoss()
def __init__(self,
A,
As,
all_nodes_neighbors,
N,
d,
layers,
steps,
delay,
weight,
GPU=False):
super(MihGNNEmbedding12, self).__init__()
self.A = Matrix_pre_handle(A, steps, delay)
self.A = torch.tensor(self.A, dtype=torch.float)
self.d = d
self.e = torch.tensor(math.e, dtype=torch.float)
self.layers = layers
self.As = As
self.weight = torch.tensor(weight, dtype=torch.float)
self.all_nodes_neighbors = all_nodes_neighbors
embedding_state = numpy.random.randn(N, d)
embedding_state = torch.tensor(data=embedding_state, dtype=torch.float)
self.aggregationModule = MihGNNAggregationModule2(
A=self.A,
As=As,
all_nodes_neighbors=all_nodes_neighbors,
convolution_layers=layers,
d=d,
embedding_states=embedding_state)
self.liner = LineNetwork(input_features=d * 2,
output_features=2,
hidden_features=d)
self.soft_max = nn.Softmax(dim=-1)
self.cross_entropy = nn.CrossEntropyLoss(weight=self.weight,
reduction='sum')
def __init__(self,
G,
A,
As,
all_nodes_neighbors,
N,
d,
walk_length,
p,
q,
iter,
window_size,
workers,
layers,
steps,
delay,
GPU=False):
super(MihGNNEmbedding12AferRandomWalk, self).__init__()
self.A = Matrix_pre_handle(A, steps, delay)
self.A = torch.tensor(self.A, dtype=torch.float)
self.d = d
self.e = torch.tensor(math.e, dtype=torch.float)
self.layers = layers
self.As = As
self.all_nodes_neighbors = all_nodes_neighbors
print("Getting word embedding!")
node2Vec = Node2vec(G=G,
A=A,
walk_length=walk_length,
p=p,
q=q,
embed_size=d,
iter=iter,
window_size=window_size,
workers=workers)
embedding_state = node2Vec.word_embeddings
# embedding_state = numpy.random.randn(N, d)
# embedding_state = torch.tensor(data=embedding_state, dtype=torch.float)
self.aggregationModule = MihGNNAggregationModule2(
A=self.A,
As=As,
all_nodes_neighbors=all_nodes_neighbors,
convolution_layers=layers,
d=d,
embedding_states=embedding_state)
self.liner = LineNetwork(input_features=d * 2,
output_features=2,
hidden_features=d)
self.soft_max = nn.Softmax(dim=-1)
# self.cross_entropy = nn.CrossEntropyLoss(weight = self.weight, reduction = 'sum')
self.cross_entropy = nn.CrossEntropyLoss()