  How can we solve semi-supervised node classification in various graphs
possibly with noisy features and structures? Graph neural networks (GNNs) have
succeeded in many graph mining tasks, but their generalizability to various
graph scenarios is limited due to the difficulty of training, hyperparameter
tuning, and the selection of a model itself. Einstein said that we should "make
everything as simple as possible, but not simpler." We rephrase it into the
careful simplicity principle: a carefully-designed simple model can surpass
sophisticated ones in real-world graphs. Based on the principle, we propose
SlimG for semi-supervised node classification, which exhibits four desirable
properties: It is (a) accurate, winning or tying on 10 out of 13 real-world
datasets; (b) robust, being the only one that handles all scenarios of graph
data (homophily, heterophily, random structure, noisy features, etc.); (c) fast
and scalable, showing up to 18 times faster training in million-scale graphs;
and (d) interpretable, thanks to the linearity and sparsity. We explain the
success of SlimG through a systematic study of the designs of existing GNNs,
sanity checks, and comprehensive ablation studies.
