Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on isomorphism tasks and countable feature spaces. We extend this theoretical framework to include continuous features - which occur regularly in real-world input domains and within the hidden layers of GNNs - and we demonstrate the requirement for multiple aggregation functions in this setting. Accordingly, we propose Principal Neighbourhood Aggregation (PNA), a novel architecture combining multiple aggregators with degree-scalers (which generalize the sum aggregator). Finally, we compare the capacity of different models to capture and exploit the graph structure via a benchmark containing multiple tasks taken from classical graph theory, which demonstrates the capacity of our model.

翻译：图表神经网络(GNNs)被证明是图表结构数据不同预测任务的有效模型。最近有关其表达力的工作侧重于异形任务和可计数的特征空间。我们扩展了这一理论框架,以包括连续特征 — — 经常发生在现实世界输入领域和GNNs隐藏层中 — — 并且我们在此环境中展示了多重聚合功能的要求。因此,我们提议了主要居民区聚合(PNA),这是一个新颖的结构,将多个聚合器与学位标尺器结合起来(该结构将总和聚合器加以概括 ) 。 最后,我们比较了不同模型通过包含古典图形理论的多项任务的基准来捕捉和利用图形结构的能力,该基准显示了我们模型的能力。