GNN that is sensitive to various distribution shifts.

Arya et al. suggest using either supervised or self-supervised learning techniques to train the NN; the former requires access to exact inference

Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.