Question on weighted graph

[xianshu1]

I have a weighted graph, where each edge has weight assign to it. Is it possible to incorporate edge weight into the learning as well?
If yes, could you advise how shall I do this? Thank you.

[acbull]

Hi.

Our model indeed supports encode edge information. In our case, we treat discrete timestamp as the edge feature and adopt a temporal encoding (

pyHGT/pyHGT/conv.py

Line 283 in 16547c0

class RelTemporalEncoding(nn.Module):

) to encode it, which will be added to the source node when passing to the target.

If your feature is continuous (like weight), you can simply adopt another encoding method to project them into a d-dimensional vector, and replace the sinusoid embedding table in the RTE, and then the weights (edge feature) could be encoded.

[xianshu1]

Thank you for your quick reply. This helps a lot.
Do you have suggestions on what encoding function would work well for continuous feature (like weight) to project them into a d-dimensional vector?

[acbull]

You can try this paper: http://proceedings.mlr.press/v119/liu20n/liu20n.pdf

[xianshu1]

Thank you for the reference.

I am wondering if an alternative way to incorporate edge weight would be multiply edges weights to the heterogeneous mutual attention coefficients prior to apply softmax?

[acbull]

Let's doable, but I guess that's not the optimal solution, as the model should learn the attention by itself. If you wants to do that, simply reply the temporal stamp as weght vector, then product it before softmax

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