Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superiority of these methods is usually supported by either evaluating their performance on small subset of benchmark datasets or by reasoning about their expressive power in terms of certain graph isomorphism tests. In this paper we critically analyse both these aspects through a transductive setting for the task of node classification. First, we delve deeper into the case of multi-label node classification which offers a more realistic scenario and has been ignored in most of the related works. Through analysing the training dynamics for GNN methods we highlight the failure of GNNs to learn over multi-label graph datasets even for the case of abundant training data. Second, we show that specifically for transductive node classification, even the most expressive GNN may fail to learn in absence of node attributes and without using explicit label information as input. To overcome this deficit, we propose a straightforward approach, referred to as GNN-MultiFix, that integrates the feature, label, and positional information of a node. GNN-MultiFix demonstrates significant improvement across all the multi-label datasets. We release our code at https://anonymous.4open.science/r/Graph-MultiFix-4121.

翻译：图神经网络（GNNs）已成为学习图数据表示的有力模型，在各种任务中展现出最先进的性能。然而，这些方法的优越性通常仅通过其在少量基准数据集子集上的评估结果，或基于特定图同构测试的表达能力推理来支持。本文通过节点分类的转导设置，对这两个方面进行了批判性分析。首先，我们深入探讨了多标签节点分类这一更具现实意义但多数相关研究忽略的场景。通过分析GNN方法的训练动态，我们揭示了GNN在多标签图数据集上即使存在充足训练数据时仍难以有效学习的问题。其次，我们证明在转导式节点分类任务中，即使最具表达力的GNN，若缺乏节点属性且未使用显式标签信息作为输入，也可能无法有效学习。为克服这一缺陷，我们提出一种名为GNN-MultiFix的简洁方法，该方法整合了节点的特征、标签及位置信息。GNN-MultiFix在所有多标签数据集上均表现出显著性能提升。代码发布于https://anonymous.4open.science/r/Graph-MultiFix-4121。