Batch inner product decoder for VGAE
See original GitHub issueHi,
Do you guys have any idea on how to implement an inner product decoder for architectures such as VGAE (Eq. 2) using mini-batches in torch_geometric
?
Thank you!
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- Created 5 years ago
- Comments:11 (6 by maintainers)
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Batch inner product decoder for VGAE · Issue #17 - GitHub
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Thank you for your reply, @rusty1s !
After a few changes in your code, it was possible to make it. Here’s what worked for me:
I couldn’t make the line
torch.matmul(assignment, assignment.t())
work for sparse matrices. It returned the following error:RuntimeError: Sparse tensors do not have strides.
I suppose this way is not the optimal, since performing dense matrix operations instead of sparse ones will take more time.
If I‘m not mistaken, masking could be achieved by:
You can then train against your densified input graph. However, this involves computing and comparing two
N x N
dense matrices, but this is more a problem of VGAE than with the proposed approach.