  The message passing framework is the foundation of the immense success
enjoyed by graph neural networks (GNNs) in recent years. In spite of its
elegance, there exist many problems it provably cannot solve over given input
graphs. This has led to a surge of research on going "beyond message passing",
building GNNs which do not suffer from those limitations -- a term which has
become ubiquitous in regular discourse. However, have those methods truly moved
beyond message passing? In this position paper, I argue about the dangers of
using this term -- especially when teaching graph representation learning to
newcomers. I show that any function of interest we want to compute over graphs
can, in all likelihood, be expressed using pairwise message passing -- just
over a potentially modified graph, and argue how most practical implementations
subtly do this kind of trick anyway. Hoping to initiate a productive
discussion, I propose replacing "beyond message passing" with a more tame term,
"augmented message passing".
