congweilin / dgcn Goto Github PK

Hi, I found you provide Decoupled Conv in layers.py, but the models.py do not contain the code of DGCN model. Also, is \beta trainable, or is it a hyperparameter?

Hi,

I just find that the implementation of APPNP is not the same as the standard design.

https://github.com/CongWeilin/DGCN/blob/master/model.py#L243

In your implementation, the node features only go through a linear transform (self.linear_in), then apply PPR propagation. Finally, it followed by another linear transformation (self.linear_out).

However, this departs from the original APPNP design.

https://github.com/klicperajo/ppnp/blob/master/ppnp/pytorch/ppnp.py

In the original design, the authors of APPNP first transform node features into C dimensional hidden representation using MLP (C is the number of classes). Then they propagate based on PPR. We can also clearly see from their illustration figure in https://github.com/klicperajo/ppnp that your implementation is not the same. I wonder how will this affect the results in your paper?

Thanks,
Eli