Per the authors, Graph Isomorphism Network (GIN) generalizes the WL test and hence achieves maximum discriminative power among GNNs.

Conditional Flow Matching (CFM) is a fast way to train continuous normalizing flow (CNF) models. CFM is a simulation-free training objective for continuous normalizing flows that allows conditional generative modelling and speeds up training and inference.

Flow Matching

Flow Matching for Generative Modeling

How Powerful are Graph Neural Networks?

A **PyramidNet** is a type of convolutional network where the key idea is to concentrate on the feature map dimension by increasing it gradually instead of by increasing it sharply at each residual unit with downsampling. In addition, the network architecture works as a mixture of both plain and residual networks by using zero-padded identity-mapping shortcut connections when increasing the feature map dimension.

Source	How Powerful are Graph Neural Networks?
Year	2000
Data Source	Papers with Code archive (CC BY-SA)