NAN loss problem[SOLVED]: Performance Plummets When Setting adj_label Diagonal to Zero

[ChildishBob]

After setting the diagonal of adj_label to zero:

The test accuracy for cora goes down to 0.13 with the same configuration:

which is quite confusing since this modification is in accordance with:

[yanghu819]

Thank you so much for your issue on the NAN loss in training：

We fix the bug, for the code will bring NAN loss in training because "log(0)" in

We add 1e-8 to prevent the NAN loss. Now the code is updated, please try it.

Also, you don't have to add the code for we remove the distance of its self in get_feature_dis in models.py

[ChildishBob]

Thanks for your work! By the way, you said

Also, you don't have to add the code for we remove the distance of its self in get_feature_dis in models.py

However, in

even when x_dis is 0, we have e^0 = 1. That conflicts with

[yanghu819]

I think you did a very good job in finding this minute difference. That indeed will result a bit difference in formula，but the loss function optimized won' t be influenced by this operation: an added constant value will not influence the gradient much, also, you can try to change it into a better formula which can be far better than this demo. In fact, even removing the mask to avoid computing loss function of loss_ii won't do harm to the final results.