def forward(self, x, edge_index):
# type: (Tensor, Tensor) -> Tensor
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
row, col = edge_index[0], edge_index[1]
out = scatter_mean(x[col], row, dim=0) # do not set dim_size, out.size() = row.max() + 1
x = x[0:out.size(0)]
x = torch.cat([x, out], dim=1)
out = torch.matmul(x, self.weight)
out = out + self.bias
out = F.normalize(out, p=2.0, dim=-1)
return out
def forward(self, x, edge_index):
# type: (Tensor, Tensor) -> Tensor
""""""
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
x = x.unsqueeze(-1) if x.dim() == 1 else x
row, col = edge_index[0], edge_index[1]
x = torch.matmul(x, self.weight)
out = scatter_mean(x[col], row, dim=0, dim_size=x.size(0))
if self.bias is not None:
out = out + self.bias
if self.normalize:
out = F.normalize(out, p=2.0, dim=-1)
return out
def forward(self, x, edge_index, edge_weight=None):
# type: (Tensor, Tensor, Optional[Tensor]) -> Tensor
edge_index, edge_weight = remove_self_loops(edge_index, edge_weight)
row, col = edge_index[0], edge_index[1]
num_nodes, num_edges, K = x.size(0), row.size(0), self.weight.size(0)
if edge_weight is None:
edge_weight = torch.ones((num_edges,),
dtype=x.dtype,
device=edge_index.device)
edge_weight = edge_weight.view(-1)
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
# Compute normalized and rescaled graph Laplacian
deg = deg.pow(-0.5)
lap = -deg[row] * edge_weight * deg[col]
# Perform filter operation recurrently
Tx_0 = x
out = torch.mm(Tx_0, self.weight[0])
Tx_1 = spmm(edge_index, lap, num_nodes, x)
if K > 1:
out = out + torch.mm(Tx_1, self.weight[1])
for k in range(K):
if k >= 2:
Tx_2 = 2 * spmm(edge_index, lap, num_nodes, Tx_1) - Tx_0
out = out + torch.mm(Tx_2, self.weight[k])
Tx_0, Tx_1 = Tx_1, Tx_2
if self.bias is not None:
out = out + self.bias
return out
def forward(self, x, edge_index):
# type: (Tensor, Tensor) -> Tensor
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
x = torch.mm(x, self.weight).view(-1, self.heads, self.out_channels)
return self.propagate(edge_index, x=x, num_nodes=x.size(0))
def remove_self_loops(self): # algorithms tend to work better if there is no self-loop in the given graph, so we call this method at first. utils.remove_self_loops(self.graph)