non-square adj matrix

[ahyunSeo]

Hello, thank you for your great work.

I want to extend gcn which involves message passing,
but I'm new to GCN so I have a minor question.

I have to types of node A, B.
Basically I want to train different weights jointly. (Weight_AA, Weight_AB, Weight_BA, Weight_BB)
During the node representation update,
A(t+1) = Weight_AA*A(t)adj(AA) + Weight_ABB(t)adj(BA)
B(t+1) = Weight_BBB(t)adj(BB) + Weight_BAA(t)*adj(AB)
The first terms are simple graph convolution layer with adj(AA), adj(BB) are both square
but for the adj(BA), adj(AB) it might not be square, (# of two types of nodes will differ)

Can I use non-square adj matrix during the whole process? (normalize, forward, ...)

[tkipf]

Thanks for your kind words. The same issue came up in our work on Graph Convolutional Matrix Completion. The implementation is available here: https://github.com/riannevdberg/gc-mc Essentially, you can construct non-square matrices for this purpose, but you will have to use left-normalization, i.e. simple division by the node degree in this case.

…

On Wed 17. Oct 2018 at 09:08 ahyunSeo ***@***.***> wrote: Hello, thank you for your great work. I want to extend gcn which involves message passing, but I'm new to GCN so I have a minor question. I have to types of node A, B. Basically I want to train different weights jointly. (Weight_AA, Weight_AB, Weight_BA, Weight_BB) During the node representation update, A(t+1) = Weight_AA*A(t)*adj(AA) + Weight_AB*B(t) *adj(BA) B(t+1) = Weight_BB*B(t)*adj(BB) + Weight_BA*A(t)*adj(AB) The first terms are simple graph convolution layer with adj(AA), adj(BB) are both square but for the adj(BA), adj(AB) it might not be square, (# of two types of nodes will differ) Can I use non-square adj matrix during the whole process? (normalize, forward, ...) — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#24>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AHAcYAND07BJ08GxhRel4zeJhxKEy2e2ks5uluV8gaJpZM4XjW4g> .

[ahyunSeo]

Do you mean by this line?
https://github.com/riannevdberg/gc-mc/blob/master/gcmc/preprocessing.py#L99

Also, thank you for sharing another good implementation.

[tkipf]

Yes

…

On Thu 18. Oct 2018 at 11:08 ahyunSeo ***@***.***> wrote: Do you mean by this line? https://github.com/riannevdberg/gc-mc/blob/master/gcmc/preprocessing.py#L99 Also, thank you for sharing another good implementation. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#24 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AHAcYMT9fw61dxVJBmwmyAaJ8hLJ9gwXks5umFMBgaJpZM4XjW4g> .