a question about class GraphConvolution in layers.py

[AllenWu18]

Hi, may I ask you a simple question?
I think in the original paper "Semi-Supervised Classification With Graph Convolutional NetworkS" by Thomas N.Kipf and Max Welling in ICLR 2017, the authors wrote that the GCN block is D^(-1/2)AD^(-1/2)XW, which D and A have added the self-loop.
And I just don't know where the operation [D^(-1/2)AD^(-1/2)] is in the code??
Is it in the function reset_parameter??
Thx!

[vmasrani]

Yup, it's here: https://github.com/vmasrani/gae_in_pytorch/blob/2639fbccb19cc1cfa17407dd36fac4917401a903/preprocessing.py#L20

[AllenWu18]

OK, I got it already :)
and I have other two questions here:

1. if we use the GAE to get the embeddings of each nodes in the graph, should we use the activation function like ReLU or not in " adj_hat = torch.mm(x,x.t()), which x is the encoder from GCN?
2. I read other codes, in reset_parameters of layers.py, some others did like following:
   def reset_parameters(self):
   stdv = 1. / math.sqrt(self.weight.size(1))
   self.weight.data.uniform_(-stdv, stdv)
   if self.bias is not None:
   self.bias.data.uniform_(-stdv, stdv)
   (from https://github.com/sbonner0/gae_in_pytorch/blob/master/layers.py)
   or
   def reset_parameters(self):
   torch.nn.init.xavier_uniform_(self.weight)
   (from https://github.com/zfjsail/gae-pytorch/blob/master/gae/layers.py)
   And do they have the similar effects like yours?? This just made me confused for a long time :(
   Thank you very much !