def __init__(self):
super(Net, self).__init__()
self.embedding = torch.nn.Parameter(torch.Tensor(data.num_nodes, embedding))
self.embedding.data.normal_()
self.conv1 = AGNNConv(requires_grad=False)
self.conv2 = AGNNConv(requires_grad=True)
self.mclp = multiClassInnerProductDecoder(embedding, data.num_classes)
def __init__(self):
super(Net, self).__init__()
self.embedding = Parameter(torch.Tensor(data.num_nodes, 256))
self.embedding.data.normal_()
self.conv1 = GATConv(256, hidden_features, heads=heads)
self.conv2 = GATConv(hidden_features * heads, embeddings, concat=False)
self.mclp = multiClassInnerProductDecoder(embeddings, data.num_classes)
def __init__(self):
super(Net, self).__init__()
self.embedding = Parameter(torch.Tensor(data.num_nodes, 256))
self.embedding.data.normal_()
self.conv1 = GCNConv(256, hidden_size)
self.conv2 = GCNConv(hidden_size, args.embedding)
self.mclp = multiClassInnerProductDecoder(args.embedding,
data.num_classes)
def __init__(self):
super(Net, self).__init__()
self.embedding = torch.nn.Parameter(
torch.Tensor(data.num_nodes, in_dim))
self.embedding.data.normal_()
self.conv1 = RGCNConv(in_dim,
hidden_size,
data.num_relations,
num_bases=data.num_relations)
self.conv2 = RGCNConv(hidden_size,
embedding,
data.num_relations,
num_bases=data.num_relations)
self.mclp = multiClassInnerProductDecoder(embedding, data.num_classes)