  Seminal works on graph neural networks have primarily targeted
semi-supervised node classification problems with few observed labels and
high-dimensional signals. With the development of graph networks, this setup
has become a de facto benchmark for a significant body of research.
Interestingly, several works have recently shown that in this particular
setting, graph neural networks do not perform much better than predefined
low-pass filters followed by a linear classifier. However, when learning from
little data in a high-dimensional space, it is not surprising that simple and
heavily regularized methods are near-optimal. In this paper, we show
empirically that in settings with fewer features and more training data, more
complex graph networks significantly outperform simple models, and propose a
few insights towards the proper choice of graph network architectures. We
finally outline the importance of using sufficiently diverse benchmarks
(including lower dimensional signals as well) when designing and studying new
types of graph neural networks.
