Embedds should add X once or twice?

[zhengwang100]

In your paper,  Eq. 13 indicates: embedds =  aX + (1-a)(TX+T^{2}X+....); i.e., you add X only once?
But in the code function "sgc_precompute", embedds = aX +(1-a)(X+TX+T^{2}X+....);  i.e., you add X twice?
Which one is correct? or do I misunderstanding? 

[allenhaozhu]

In our paper, to demonstrate an APPNP similar form, we add X for every propagation. However, it is easy to move X outside the sum function and then add X once in the implementation.

[allenhaozhu]

In your paper,  Eq. 13 indicates: embedds =  a_X + (1-a)(TX+T^{2}X+....); i.e., you add X only once? But in the code function "sgc_precompute", embedds = a_X +(1-a)(X+TX+T^{2}X+....);  i.e., you add X twice?
Which one is correct? or do I misunderstanding?

Thanks for your comments because they may help me improve the camera-ready version.

[wsunid]

Hi, thanks for the code!

Do I misunderstand the code? Have you raised the power to the (1 - alpha)? in which case, embeds = ax + 1/k (x + (1 - a)T x + (1-a)T(1-a)Tx ...). To be simple, the equation in my understanding becomes: ax + 1/k (x + (1 - a)T x + (1-a)^2 T^2 Tx ...(1-a)^k T^k x)

SSGC/utils.py

Line 98 in 115d483

features = (1-alpha) * torch.spmm(adj, features)

[allenhaozhu]

Hi, thanks for the code!

Do I misunderstand the code? Have you raised the power to the (1 - alpha)? in which case, embeds = ax + 1/k (x + (1 - a)T x + (1-a)T(1-a)Tx ...). To be simple, the equation in my understanding becomes: ax + 1/k (x + (1 - a)T x + (1-a)^2 T^2 Tx ...(1-a)^k T^k x)

SSGC/utils.py

Line 98 in 115d483

features = (1-alpha) * torch.spmm(adj, features)

looks like you consider i=0 but there is no item for this because we move this item out of the sum.

[wsunid]

Maybe I miss some information or I made some mistakes here.

Are we supposed to do ax + (1-a)* 1/k *(T x + T^2 Tx ...T^k x) right? I guess the code at line 98-99 should be something like:

features = torch.spmm(adj, features)
emb += (1- alpha) * features

[allenhaozhu]

Maybe I miss some information, or I made some mistakes here.

Are we supposed to do ax + (1-a)* 1/k *(T x + T^2 Tx ...T^k x) right? I guess the code at line 98-99 should be something like:

features = torch.spmm(adj, features)
emb += (1- alpha) * features

I got the point............Sorry for the problem of this code. Thank you very much for this issue.
def sgc_precompute(features, adj, degree, alpha): t = perf_counter() feature_ori = features feature_set = torch.zeros_like(features) for i in range(degree): features = torch.spmm(adj, features) feature_set += (1-alpha)*features + alpha*feature_ori #feature_set/=degree+1 feature_set /= degree #build for PPNA like 0.15 for cora and citeseer
I attached the answer to another issue. It is the code we experiment with the paper. I will update this code as soon as possible. (These days UAI rebuttal is coming and ACMMM deadline is coming)
I really need to rewrite a new version as I said in another issue.

[wsunid]

Thanks!