susheels / adgcl Goto Github PK

Hi, thanks for your great work!
I have a question about the detail of NAD-GCL.
In 5.1 of the paper it writes:

NAD-GCL drops the edges of a graph uniformly at random. We consider NAD-GCL-FIX and NAD-GCL-OPT with different edge drop ratios. NAD-GCL-GCL adopts the edge drop ratio of AD-GCL-FIX at the saddle point of the optimization (Eq.8) while NAD-GCL-OPT optimally tunes the edge drop ratio over the validation datasets to match AD-GCL-OPT.

I didn't quite understand how to define the edge drop ratio in NAD-GCL.
What's the difference between NAD-GCL and GraphCL which using EdgePert?
Thank you!

Hi @susheels

Thanks for the great work. I tried to reproduce the transfer learning results of AD-GCL. Concretely, I pretrained the model on the ZINC-2M for 100 epochs, and fine-tuned it on the downstream tasks. However, the reproduced results are lower than ones in the paper. Could you pls help me with it? Thanks!

Hi, thanks for your excellent work!
I find that you evaluate adgcl mainly on graph-level task with multiple datasets. And I wonder whether adgcl can be applied on a single graph dataset like Cora or Citeseer for node classification?

Hi @susheels

Thanks for your great work. I have a minor request that could you pls release the code of baselines (e.g., GraphCL) for OGBG. I think it's a bit difficult to adapt the test_minimax_ogbg.py directly. It's really helpful if you could release them. Thanks a lot!

Hi! Could you please your pretrained model files of transfer learning(bio and chem dataset)? Thus I can use it for finetuning and better approximate the results of transfer learning in your paper.

Hi!
I'm trying to reproduce your AD-GCL results on TU-dataset (unsupervised learning). I could achieve nearly the same results as your paper reported. However, when I split the training process into two steps (AD_GCL for latent vector generation and using latent vector for linear classification (linear SVC). The result is bad enough (training : 65%, val and test: ~50%)). I wonder what is the difference between these two training strategies.
Thanks a lot