Unify duplicate implementation of CAN

[ninamiolane]

Unify duplicate implementation of CAN

[mhajij]

@ninamiolane

the following note was left on the notebook

Notes:

The attention function provided for us does not normalize the attention coefficients.

Should this be done?

Where should we be able to customize the non-linearities?

Seems important for the output.

What about the attention non-linearities do we just use what is given?

I wanted to make this so that without attention it ends up being

the Hodge Laplacian network.

Maybe ask the contest organizers about this?

I agree on the above. Attention in the base layer is used in different context typically used in the lit.

lit : it means learnable parameter that is multiplied by the original vector during training. Sum = 1 for a given neighborhood(x)
base layer in TopoModelX : a coefficient in the nbhd matrix and it can be +1 or -1 depending on the orientation.

In general the layer is not faithful to the CAN implementation so I recommend deleting it from the package.

[ninamiolane]

Sounds good. The layer that is not faithful to the CAN implementation can be deleted.

Can you open a PR that does it? Once that PR is merged, and the corresponding CAN code is removed, we can mark this task as done.

[ninamiolane]

Completed through PR #192 and PR #182