  Graph classification receives a great deal of attention from the
non-Euclidean machine learning community. Recent advances in graph coarsening
have enabled the training of deeper networks and produced new state-of-the-art
results in many benchmark tasks. We examine how these architectures train and
find that performance is highly-sensitive to initialisation and depends
strongly on jumping-knowledge structures. We then show that, despite the great
complexity of these models, competitive performance is achieved by the simplest
of models -- structure-blind MLP, single-layer GCN and fixed-weight GCN -- and
propose these be included as baselines in future.
