tkipf
commented
4 years ago You can implement the message passing operation more efficiently using the torch.scatter_add operator. You will need to provide an edge list to this function, but this should be easy to pre-compute. Note that you will have to enforce some kind of sparsity template on the graph, i.e. you will most likel y have to consider only a subset of the N^2 edges in the graph as 10.000^2 is likely going to be way too much both in terms of memory and computational complexity.
On Sat 3. Aug 2019 at 07:07 Omar notifications@github.com wrote:
Many thanks for the interesting work. Indeed, I am trying to use your model on large biological graphs (more than 10K nodes) but I am facing memory limits. Basically, you are using the one-hot encoding for all the edges in a fully connected graph to exchange the messages and to facilitate the optimization of the ELBO. For very large graphs such encoding is not an option. I tried using sparse tensors but the missing strides for torch.matmul (requires contiguous representation for the data) and the unsupported broadcasting for matrix multiplication with torch.mm limited my efforts to patch your implementation. Do you have please an idea on how we could extend the application of your model on large graphs? Thank you very much in advance.
— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/ethanfetaya/NRI/issues/19?email_source=notifications&email_token=ABYBYYF57MC2MZNUDBERBO3QCUG7RA5CNFSM4IJCHWBKYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HDFRJ7A, or mute the thread https://github.com/notifications/unsubscribe-auth/ABYBYYADE64BVAO3USOTGHLQCUG7RANCNFSM4IJCHWBA .
omarmaddouri
Many thanks for the interesting work. Indeed, I am trying to use your model on large biological graphs (more than 10K nodes) but I am facing memory limits. Basically, you are using the one-hot encoding for all the edges in a fully connected graph to exchange the messages and to facilitate the optimization of the ELBO. For very large graphs such encoding is not an option. I tried using sparse tensors but the missing strides for torch.matmul (requires contiguous representation for the data) and the unsupported broadcasting for matrix multiplication with torch.mm limited my efforts to patch your implementation. Do you have please an idea on how we could extend the application of your model on large graphs? Thank you very much in advance.