  In this work we formalize the (pure observational) task of predicting node
attribute evolution in temporal graphs. We show that node representations of
temporal graphs can be cast into two distinct frameworks: (a) The de-facto
standard approach, which we denote {\em time-and-graph}, where equivariant
graph (e.g., GNN) and sequence (e.g., RNN) representations are intertwined to
represent the temporal evolution of the graph; and (b) an approach that we
denote {\em time-then-graph}, where the sequences describing the node and edge
dynamics are represented first (e.g., RNN), then fed as node and edge
attributes into a (static) equivariant graph representation that comes after
(e.g., GNN). In real-world datasets, we show that our {\em time-then-graph}
framework achieves the same prediction performance as state-of-the-art {\em
time-and-graph} methods. Interestingly, {\em time-then-graph} representations
have an expressiveness advantage over {\em time-and-graph} representations when
both use component GNNs that are not most-expressive (e.g., 1-Weisfeiler-Lehman
GNNs). We introduce a task where this expressiveness advantage allows {\em
time-then-graph} methods to succeed while state-of-the-art {\em time-and-graph}
methods fail.
