def generate_normalization(self, norm="sym"):
if self.__normed__:
return
degrees = (self.row_ptr[1:] - self.row_ptr[:-1]).float()
if norm == "sym":
edge_norm = torch.pow(degrees, -0.5).to(self.device)
edge_norm[torch.isinf(edge_norm)] = 0
self.__out_norm__ = self.__in_norm__ = edge_norm.view(-1, 1)
elif norm == "row":
edge_norm = torch.pow(degrees, -1).to(self.device)
edge_norm[torch.isinf(edge_norm)] = 0
self.__out_norm__ = None
self.__in_norm__ = edge_norm.view(-1, 1)
elif norm == "col":
self.row, _, _ = csr2coo(self.row_ptr, self.col, self.weight)
self.weight = row_normalization(self.num_nodes, self.col, self.row, self.weight)
else:
raise NotImplementedError
self.__normed__ = norm
def edge_index(self):
if self.row is None:
self.row, _, _ = csr2coo(self.row_ptr, self.col, self.weight)
return torch.stack([self.row, self.col])
def edge_index(self):
if self.row is None:
self.row, _, _ = csr2coo(self.row_ptr, self.col, self.weight)
return self.row, self.col