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.

翻译：如何解决可能带有噪声特征和结构的各类图中的半监督节点分类问题？图神经网络（GNNs）在许多图挖掘任务中取得了成功，但由于训练困难、超参数调优以及模型本身的选择，其在不同图场景下的泛化能力有限。爱因斯坦曾言：“一切应尽可能简洁，但不能过于简单。”我们将其重新表述为“审慎简洁原则”：一个精心设计的简单模型在实际图数据中能够超越复杂的模型。基于这一原则，我们提出了用于半监督节点分类的SlimG，它具有四个理想特性：（a）精确性，在13个真实数据集的10个中取得最优或持平结果；（b）鲁棒性，是唯一能处理所有图数据场景（同质性、异质性、随机结构、噪声特征等）的方法；（c）快速可扩展性，在百万量级图上的训练速度提升高达18倍；（d）可解释性，得益于其线性和稀疏性。我们通过对现有GNN设计的系统性研究、合理性检查以及全面的消融实验，解释了SlimG的成功。