Hi, I have two questions w.r.t Pm_Pd
implemented in DGL-LGNN. I read the original paper, and think the matrices pm
and pd
should be the forms in this link, respectively. So what the form is Pm_Pd
in DGL
since it is loaded from a pickel file in the source code?
Another question is in LGNN
:
def forward(self, g, lg, pm_pd):
pm_pd
is shown directly to forward
method rather than computed in the feed forward process, then pm_pd
should be batched before training. We can view a batched graph as several unlinked graphs, however as an example, when batch process pm
, it seems each pm
of the unlinked graphs should be a block in the huge matrix, which could be implemented with torch.block_diag
. Finally the batched Pm
matrix is a block matrix. Is that right?