Hi ! Thanks a lot for this awesome project and paper, could you please give some package version information corresponding to the environment that you successfully run the project?I have some trouble due to the incorrect package version.

Hello! I have one more confusing issue now...
As you wrote in Formula (11) and (12) (Section 2.5.2) of your paper published on WWW'21, the disagreement loss should measure the intra- and inter-view's disagreement on UNLABELED drug pairs.

But in your implementation, the disagreement quantified by KL divergence is calculated on the LABELED drug pairs using the adj_mask rather than adj_mask_un . The adj_mask_un is never used in loss computation. Is this a bug?

Hello，I download your datasets, and found that the DDIs number of ZhangDDI is double, but your ChCH datasets didn't double, So what is the stardand?

I have constructed a new DDI dataset of larger scale than the ones you provided. I got confused that using the GCN(for learning from observed DDIs) from MIRACLE only gained better performance than using the entire MIRACLE.
With no outputs from BAMPN (for learning from molecular graphs) or other node information, I just initialized node features for the first GCN layer by standard normal distribution.
I have tried for times, but with no exception, the performance GCN-only > BAMPN+GCN > BAMPN-only.
How to tune the hyperparameters to make the intra- and inter- view fused better?
Any help? Thanks in advance.

Hello authors
thank you for your great work predicting drug-drug interactions!

I have questions on the implementation.
Where can I find the interaction prediction in the code?
It seems like the model simply predict the interaction between drug i and j using only feature of i.

Thank you!
Namkyeong