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Home Page: https://arxiv.org/abs/2112.02472
The official source code for "Augmentation-Free Self-Supervised Learning on Graphs"
Home Page: https://arxiv.org/abs/2112.02472
Thanks for your great work fiirst! But the experiment result of BGRL on Wiki-CS dataset of your paper is much inconsistent with the BGRL paper and your own experiment result in repository BGRL_Pytorch. Did I make any stupid mistakes?
It seems like a problem with the dataset "wikics", but i don't know where the problem is.
Here the stack is:
Exception has occurred: FileNotFoundError
[Errno 2] No such file or directory: 'data/pyg/WikiCS/processed/byg.data.pt'
File "/home/wanguancheng/github/AFGRL/data.py", line 64, in init
self.data, self.slices = torch.load(path)
File "/home/wanguancheng/github/AFGRL/models/AFGRL.py", line 45, in _init
self._dataset = Dataset(root=args.root, dataset=args.dataset)
File "/home/wanguancheng/github/AFGRL/models/AFGRL.py", line 33, in init
self._init()
File "/home/wanguancheng/github/AFGRL/main.py", line 22, in main
embedder = AFGRL_ModelTrainer(args)
File "/home/wanguancheng/github/AFGRL/main.py", line 28, in
main()
Thank you for providing your code!
I had a question about your logistic regression implementation. I noticed that the LogisticRegression
class consists of a single nn.Linear
layer without applying the logistic function.
Although the logistic function is order-preserving (and therefore will not change the end result after training since you take the argmax anyways), it seems not applying the logistic function may affect the stability of the convergence of your method. This would also seem to make your classifier a linear transform instead of logistic regression. Was this an intentional decision in your experimental design (e.g. were there better results without the logistic function?), or was it an oversight?
Thanks!
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