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hi,sorry to interrupt you again~
we can see you get a so big improvement on amazon dataset !
i run your code on amazon dataset , and search parameters as your paper said "α; β; from 0:0001; 0:001; 0:01; 0:1".
but i am sad i can not get a comparable result as your paper. some details is below: nmi: my best result is 0.2858 | 0.2954 for DMGI-att and DMGI. your paper is 0.412 and 0.425
macro | micro : my best result is 0.7426 | 0.7462| 0.7380| 0.7414 for DMGI-att and DMGI. your paper is
0.758 | 0.758 | 0.746 | 0.748. We can see my result is very close to you .
sim: my best result is 0.820 and 0.809 for DMGI-att and DMGI. , your paper is 0.825 and 0.816 . we can see my result is also very close to you .
so i am very confused about the performance about the metric nmi. do you meet the same problem or can you give me some suggestions?
The text was updated successfully, but these errors were encountered:
hi,sorry to interrupt you again~
we can see you get a so big improvement on amazon dataset !
i run your code on amazon dataset , and search parameters as your paper said "α; β; from 0:0001; 0:001; 0:01; 0:1".
but i am sad i can not get a comparable result as your paper. some details is below:
nmi: my best result is 0.2858 | 0.2954 for DMGI-att and DMGI. your paper is 0.412 and 0.425
macro | micro : my best result is 0.7426 | 0.7462| 0.7380| 0.7414 for DMGI-att and DMGI. your paper is
0.758 | 0.758 | 0.746 | 0.748. We can see my result is very close to you .
sim: my best result is 0.820 and 0.809 for DMGI-att and DMGI. , your paper is 0.825 and 0.816 . we can see my result is also very close to you .
so i am very confused about the performance about the metric nmi. do you meet the same problem or can you give me some suggestions?
The text was updated successfully, but these errors were encountered: