def __init__(self,
in_channels,
hidden_channels,
num_layers,
lamb=5,
bias=True):
super(SignedGCN, self).__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.num_layers = num_layers
self.lamb = lamb
self.conv1 = SignedConv(in_channels,
hidden_channels // 2,
first_aggr=True)
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(
SignedConv(hidden_channels // 2,
hidden_channels // 2,
first_aggr=False))
self.lin = torch.nn.Linear(2 * hidden_channels, 3)
self.reset_parameters()
def __init__(self,
num_features,
n_classes,
num_hidden,
num_hidden_layers,
dropout,
activation,
first_aggr='add',
bias=True):
super(PSigned, self).__init__()
# dropout
if dropout:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = nn.Dropout(p=0.)
#activation
self.activation = activation
# input layer
self.conv_input = SignedConv(num_features,
num_hidden,
first_aggr=first_aggr,
bias=bias)
# Hidden layers
self.layers = nn.ModuleList()
for _ in range(num_hidden_layers):
self.layers.append(
SignedConv(num_hidden,
num_hidden,
first_aggr=first_aggr,
bias=bias))
# output layer
self.conv_output = SignedConv(num_hidden,
n_classes,
first_aggr=first_aggr,
bias=bias)
def test_signed_conv():
in_channels, out_channels = (16, 32)
pos_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
neg_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = pos_ei.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = SignedConv(in_channels, out_channels, first_aggr=True)
assert conv.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
out1 = conv(x, pos_ei, neg_ei)
assert out1.size() == (num_nodes, 2 * out_channels)
jit_conv = conv.jittable(x=x, pos_edge_index=pos_ei, neg_edge_index=neg_ei)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, pos_ei, neg_ei).tolist() == out1.tolist()
conv = SignedConv(out_channels, out_channels, first_aggr=False)
assert conv.__repr__() == 'SignedConv(32, 32, first_aggr=False)'
out2 = conv(out1, pos_ei, neg_ei)
assert out2.size() == (num_nodes, 2 * out_channels)
jit_conv = conv.jittable(x=out1,
pos_edge_index=pos_ei,
neg_edge_index=neg_ei)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(out1, pos_ei, neg_ei).tolist() == out2.tolist()
def test_signed_conv():
in_channels, out_channels = (16, 32)
pos_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
neg_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = pos_ei.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = SignedConv(in_channels, out_channels, first_aggr=True)
assert conv.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
x = conv(x, pos_ei, neg_ei)
assert x.size() == (num_nodes, 2 * out_channels)
conv = SignedConv(out_channels, out_channels, first_aggr=False)
assert conv.__repr__() == 'SignedConv(32, 32, first_aggr=False)'
assert conv(x, pos_ei, neg_ei).size() == (num_nodes, 2 * out_channels)
class SignedGCN(torch.nn.Module):
def __init__(self,
in_channels,
hidden_channels,
num_layers,
lamb,
bias=True):
super(SignedGCN, self).__init__()
self.in_channels = in_channels
self.conv1 = SignedConv(in_channels,
hidden_channels // 2,
first_aggr=True)
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(
SignedConv(hidden_channels // 2,
hidden_channels // 2,
first_aggr=False))
self.lin = torch.nn.Linear(2 * hidden_channels, 1)
self.reset_parameters()
def reset_parameters(self):
self.conv1.reset_parameters()
for conv in self.convs:
conv.reset_parameters()
self.lin.reset_parameters()
def create_spectral_features(self, pos_edge_index, neg_edge_index):
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=1)
N = edge_index.max().item() + 1
edge_index = edge_index.to(torch.device('cpu')).detach().numpy()
pos_val = torch.full((pos_edge_index.size(1), ), 1, dtype=torch.float)
neg_val = torch.full((neg_edge_index.size(1), ), -1, dtype=torch.float)
val = torch.cat([pos_val, neg_val], dim=0)
val = val.to(torch.device('cpu')).detach().numpy()
# Borrowed from:
# https://github.com/benedekrozemberczki/SGCN/blob/master/src/utils.py
A = scipy.sparse.coo_matrix((val, edge_index), shape=(N, N))
svd = TruncatedSVD(n_components=self.in_channels, n_iter=128)
svd.fit(A)
x = svd.components_.T
return torch.from_numpy(x).to(torch.float).to(pos_edge_index.device)
def forward(self, x, pos_edge_index, neg_edge_index):
""""""
x = F.relu(self.conv1(x, pos_edge_index, neg_edge_index))
for conv in self.convs:
x = F.relu(conv(x, pos_edge_index, neg_edge_index))
return x
def discriminate(self, z, edge_index, sigmoid=True):
value = torch.cat([z[edge_index[0]], z[edge_index[1]]], dim=1)
value = self.lin(value).view(-1)
return torch.sigmoid(value) if sigmoid else value
def loss(self, z, pos_edge_index, neg_edge_index):
pass
def test(self, z, pos_edge_index, neg_edge_index):
pass
class SignedGCN(torch.nn.Module):
r"""The signed graph convolutional network model from the `"Signed Graph
Convolutional Network" <https://arxiv.org/abs/1808.06354>`_ paper.
Internally, this module uses the
:class:`torch_geometric.nn.conv.SignedConv` operator.
Args:
in_channels (int): Size of each input sample.
out_channels (int): Size of each output sample.
num_layers (int): Number of layers.
lamb (float, optional): Balances the contributions of the overall
objective. (default: :obj:`5`)
bias (bool, optional): If set to :obj:`False`, all layers will not
learn an additive bias. (default: :obj:`True`)
"""
def __init__(self,
in_channels,
hidden_channels,
num_layers,
lamb=5,
bias=True):
super(SignedGCN, self).__init__()
self.in_channels = in_channels
self.lamb = lamb
self.conv1 = SignedConv(in_channels,
hidden_channels // 2,
first_aggr=True)
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(
SignedConv(hidden_channels // 2,
hidden_channels // 2,
first_aggr=False))
self.lin = torch.nn.Linear(2 * hidden_channels, 3)
self.reset_parameters()
def reset_parameters(self):
self.conv1.reset_parameters()
for conv in self.convs:
conv.reset_parameters()
self.lin.reset_parameters()
def split_edges(self, edge_index, test_ratio=0.2):
r"""Splits the edges :obj:`edge_index` into train and test edges.
Args:
edge_index (LongTensor): The edge indices.
test_ratio (float, optional): The ratio of test edges.
(default: :obj:`0.2`)
"""
mask = torch.ones(edge_index.size(1), dtype=torch.uint8)
mask[torch.randperm(mask.size(0))[:int(test_ratio * mask.size(0))]] = 0
train_edge_index = edge_index[:, mask]
test_edge_index = edge_index[:, 1 - mask]
return train_edge_index, test_edge_index
def create_spectral_features(self,
pos_edge_index,
neg_edge_index,
num_nodes=None):
r"""Creates :obj:`in_channels` spectral node features based on
positive and negative edges.
Args:
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`pos_edge_index` and
:attr:`neg_edge_index`. (default: :obj:`None`)
"""
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=1)
N = edge_index.max().item() + 1 if num_nodes is None else num_nodes
edge_index = edge_index.to(torch.device('cpu'))
pos_val = torch.full((pos_edge_index.size(1), ), 2, dtype=torch.float)
neg_val = torch.full((neg_edge_index.size(1), ), 0, dtype=torch.float)
val = torch.cat([pos_val, neg_val], dim=0)
row, col = edge_index
edge_index = torch.cat([edge_index, torch.stack([col, row])], dim=1)
val = torch.cat([val, val], dim=0)
edge_index, val = coalesce(edge_index, val, N, N)
val = val - 1
# Borrowed from:
# https://github.com/benedekrozemberczki/SGCN/blob/master/src/utils.py
edge_index = edge_index.detach().numpy()
val = val.detach().numpy()
A = scipy.sparse.coo_matrix((val, edge_index), shape=(N, N))
svd = TruncatedSVD(n_components=self.in_channels, n_iter=128)
svd.fit(A)
x = svd.components_.T
return torch.from_numpy(x).to(torch.float).to(pos_edge_index.device)
def forward(self, x, pos_edge_index, neg_edge_index):
"""Computes node embeddings :obj:`z` based on positive edges
:obj:`pos_edge_index` and negative edges :obj:`neg_edge_index`.
Args:
x (Tensor): The input node features.
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
"""
z = F.relu(self.conv1(x, pos_edge_index, neg_edge_index))
for conv in self.convs:
z = F.relu(conv(z, pos_edge_index, neg_edge_index))
return z
def discriminate(self, z, edge_index):
"""Given node embeddings :obj:`z`, classifies the link relation
between node pairs :obj:`edge_index` to be either positive,
negative or non-existent.
Args:
x (Tensor): The input node features.
edge_index (LongTensor): The edge indices.
"""
value = torch.cat([z[edge_index[0]], z[edge_index[1]]], dim=1)
value = self.lin(value)
return torch.log_softmax(value, dim=1)
def nll_loss(self, z, pos_edge_index, neg_edge_index):
"""Computes the discriminator loss based on node embeddings :obj:`z`,
and positive edges :obj:`pos_edge_index` and negative nedges
:obj:`neg_edge_index`.
Args:
z (Tensor): The node embeddings.
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
"""
edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=1)
none_edge_index = negative_sampling(edge_index, z.size(0))
nll_loss = 0
nll_loss += F.nll_loss(
self.discriminate(z, pos_edge_index),
pos_edge_index.new_full((pos_edge_index.size(1), ), 0))
nll_loss += F.nll_loss(
self.discriminate(z, neg_edge_index),
neg_edge_index.new_full((neg_edge_index.size(1), ), 1))
nll_loss += F.nll_loss(
self.discriminate(z, none_edge_index),
none_edge_index.new_full((none_edge_index.size(1), ), 2))
return nll_loss / 3.0
def pos_embedding_loss(self, z, pos_edge_index):
"""Computes the triplet loss between positive node pairs and sampled
non-node pairs.
Args:
z (Tensor): The node embeddings.
pos_edge_index (LongTensor): The positive edge indices.
"""
i, j, k = negative_triangle_sampling(pos_edge_index, z.size(0))
out = (z[i] - z[j]).pow(2).sum(dim=1) - (z[i] - z[k]).pow(2).sum(dim=1)
return torch.clamp(out, min=0).mean()
def neg_embedding_loss(self, z, neg_edge_index):
"""Computes the triplet loss between negative node pairs and sampled
non-node pairs.
Args:
z (Tensor): The node embeddings.
neg_edge_index (LongTensor): The negative edge indices.
"""
i, j, k = negative_triangle_sampling(neg_edge_index, z.size(0))
out = (z[i] - z[k]).pow(2).sum(dim=1) - (z[i] - z[j]).pow(2).sum(dim=1)
return torch.clamp(out, min=0).mean()
def loss(self, z, pos_edge_index, neg_edge_index):
"""Computes the overall objective.
Args:
z (Tensor): The node embeddings.
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
"""
nll_loss = self.nll_loss(z, pos_edge_index, neg_edge_index)
loss_1 = self.pos_embedding_loss(z, pos_edge_index)
loss_2 = self.neg_embedding_loss(z, neg_edge_index)
return nll_loss + self.lamb * (loss_1 + loss_2)
def test(self, z, pos_edge_index, neg_edge_index):
"""Evaluates node embeddings :obj:`z` on positive and negative test
edges by computing AUC and F1 scores.
Args:
z (Tensor): The node embeddings.
pos_edge_index (LongTensor): The positive edge indices.
neg_edge_index (LongTensor): The negative edge indices.
"""
with torch.no_grad():
pos_p = self.discriminate(z, pos_edge_index)[:, :2].max(dim=1)[1]
neg_p = self.discriminate(z, neg_edge_index)[:, :2].max(dim=1)[1]
pred = (1 - torch.cat([pos_p, neg_p])).cpu()
y = torch.cat(
[pred.new_ones((pos_p.size(0))),
pred.new_zeros(neg_p.size(0))])
pred, y = pred.numpy(), y.numpy()
auc = roc_auc_score(y, pred)
f1 = f1_score(y, pred, average='binary') if pred.sum() > 0 else 0
return auc, f1
def test_signed_conv():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv1 = SignedConv(16, 32, first_aggr=True)
assert conv1.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
conv2 = SignedConv(32, 48, first_aggr=False)
assert conv2.__repr__() == 'SignedConv(32, 48, first_aggr=False)'
out1 = conv1(x, edge_index, edge_index)
assert out1.size() == (4, 64)
assert conv1(x, adj.t(), adj.t()).tolist() == out1.tolist()
out2 = conv2(out1, edge_index, edge_index)
assert out2.size() == (4, 96)
assert conv2(out1, adj.t(), adj.t()).tolist() == out2.tolist()
if is_full_test():
t = '(Tensor, Tensor, Tensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert jit1(x, edge_index, edge_index).tolist() == out1.tolist()
assert jit2(out1, edge_index, edge_index).tolist() == out2.tolist()
t = '(Tensor, SparseTensor, SparseTensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert jit1(x, adj.t(), adj.t()).tolist() == out1.tolist()
assert jit2(out1, adj.t(), adj.t()).tolist() == out2.tolist()
adj = adj.sparse_resize((4, 2))
assert torch.allclose(conv1((x, x[:2]), edge_index, edge_index), out1[:2],
atol=1e-6)
assert torch.allclose(conv1((x, x[:2]), adj.t(), adj.t()), out1[:2],
atol=1e-6)
assert torch.allclose(conv2((out1, out1[:2]), edge_index, edge_index),
out2[:2], atol=1e-6)
assert torch.allclose(conv2((out1, out1[:2]), adj.t(), adj.t()), out2[:2],
atol=1e-6)
if is_full_test():
t = '(PairTensor, Tensor, Tensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert torch.allclose(jit1((x, x[:2]), edge_index, edge_index),
out1[:2], atol=1e-6)
assert torch.allclose(jit2((out1, out1[:2]), edge_index, edge_index),
out2[:2], atol=1e-6)
t = '(PairTensor, SparseTensor, SparseTensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert torch.allclose(jit1((x, x[:2]), adj.t(), adj.t()), out1[:2],
atol=1e-6)
assert torch.allclose(jit2((out1, out1[:2]), adj.t(), adj.t()),
out2[:2], atol=1e-6)
