opened
yanlirock
username_0
in the function normalize_adj
the output is:
"adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()"

why not using 
"d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt) "
follow by the original equation in paper?

and the results of above two are different
why need transpose in adjacency matrix normalization? 
issue
created
MeetUo
username_1
I also have this confusion.
But I find  the adj matrix is the symmetric matrix and the d_mat_inv_sqrt is the diagonal matrix .
So 
"adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)"
actually equal
"d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt) "
But I also don't know why need transpose, hope the author can answer our doubts.
comment
created
leeswan
username_2
transpose for construct symmetric normalized adj matrix in tensorflow version, while in pytorch version, the method of normalization is different, features and adj take the same way
comment
created
kentfly
username_3
comment
created
tkipf
username_4
If `adj` is symmetric (which we assume) then both are identical.
comment
created
kentfly
username_3
i get it!!
thx a looooot....:))
comment