Question on simple GCN layer of model asgcn

[eliasyin]

https://github.com/songyouwei/ABSA-PyTorch/blob/9acab7e62e8aa52a8eb0b4a560d39740bc0f3798/models/asgcn.py#L12-L33 This part of code is commented with Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 However, I cannot find the relation between the GraphConvolution with the mentioned paper SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS. Because the forward propagation formula in the mentioned paper is $$H^{l+1}=\sigma(\tilde{D}^{-\frac{1}{2}}\tildee{A}\tilde{D}^{-\frac{1}{2}})$$, while the code here just apply simple matrix multiply

[GeneZC]

We use a simplified version while original paper uses a normalized version.

[eliasyin]

But I think it's over simplified. According to the paper section 3.1 EXAMPLE,

We first calculate in a pre-processing step. Our forward model then takes the simple form:

Therefore, I think there should be a step that calculate \hat{A}. Without this step, I think the code of GraphConvolution is not Graph Convolution

[GeneZC]

It has been shown in http://tkipf.github.io/graph-convolutional-networks/ that this simplified version is already powerful. Quote: ``Despite its simplicity this model is already quite powerful''. And you can additionally implement the normalized version to see if the postulate is true.

[eliasyin]

Thank you. I see.