I’m trying to replicate the GNNExplainer.
When evaluating a trained GNN model inside the GNNExplainer with a subset of the original training dataset, some the of node returns prediction as shown below. As i try to do a softmax on it, it returns a list of very small values, which I won’t be able to use to calculate the loss…

Sorry I’m very new to GNNs and softmax function… I would appreciate if anyone can give me some advice on the softmax function. Or are the prediction values returned incorrectly?

Softmax converts raw-score logits that run from -inf to inf
into probabilities that run from 0 to 1. Note that your
“model_ypred(softmax)” sum to 1 as proper probabilities should.

The [2] element of “model_ypred(softmax)” is 99.46%, very nearly 100%, and corresponds to the largest (and only non-negative) value
in “model_ypred: tensor”.

Whether the results of Softmax should be understood as “large” or
“small” depends on what you do with them.