Comments (2)
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Hi, Thank you for your message. I just went back to my notes to find the good hyper-parameters of MUTAG. Unfortunately, I added MUTAG right before the submission and was not organised enough with my experiments at the time, so I could not find much… What I remember is that MUTAG was trickier to evaluate as ground truth explanations relies on edges (not nodes, unlike the default version of GraphSVX). So I struggled a bit more to get good results on this dataset, unlike the others. I found those settings marked somewhere but I don’t know if these are SOTA results or intermediate ones: python3 script_eval_gt.py --dataset='Mutagenicity' --num_samples=300/500 --S=3 --coal='SmarterSeparate' --hv='compute_pred' --feat=‘All' Training ⇒ 800 epochs, wd = 0.002. Loss = 0.28. Results = 0.88 0.84 0.81 (not exactly SOTA but closer than what you found I believe). I am sorry I am not able to help out more. Good luck ! Best wishes, Alexandre
…
Dear author,
Thanks for your sharing work,
Could you be more specific on the training hyper-parameters for MUTAG, such as lr, weight clip, weight decay, and batch size. The Best Accuracy I could get from the repo is about 80%.
Thanks!
from graphsvx.
Related Issues (7)
- Explanation Accuracy Much Lower Than Table in Paper for syn2/syn4/syn5 HOT 1
- Environment requirements HOT 5
- Is it useable for heterogeneous graph? HOT 2
- I don't know how this code works HOT 1
- Is GraphSVX usable for graph regression ?
- Is GraphSVX applicable to node regression tasks? (GNNs that predict a numerical value on the nodes of a graph) HOT 1
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