isjakewong / miracle Goto Github PK
View Code? Open in Web Editor NEWMulti-view Graph Contrastive Representation Learning for Drug-drug Interaction Prediction
Home Page: https://arxiv.org/abs/2010.11711
Multi-view Graph Contrastive Representation Learning for Drug-drug Interaction Prediction
Home Page: https://arxiv.org/abs/2010.11711
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! 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?
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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.
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