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[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
This project forked from mpanpan/simgrace
[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
Hi,
Thanks for sharing your code.
After reading your code (simgrace.py in unsupervised TU), I found that the perturbations are not updated and they are just simply added into the GNN model's parameters. So in this case, how can you guarantee it is a positive sample? If I do not understand correctly, please correct me.
作者您好。最近刚接触对比学习,您文章中有分析alignment和uniformity这两个指标,但我在代码部分没找到。不知道是我没找到,还是这部分是和模型分开单独计算、分析的?
您好,我看无监督实验中每个随机种子跑出向量表示后,会用k折交叉验证出多个acc,然后取均值作为该随机种子下的效果,最终论文中的平均acc我理解应该是多个随机种子的acc取均值,但标准差是怎么计算的呢,是多个随机种子下的标准差取均值吗?
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