Although recent advances in higher-order Graph Neural Networks (GNNs) improve
the theoretical expressiveness and molecular property predictive performance,
they often fall short of the empirical performance of models that explicitly
use fragment information as inductive bias. However, for these approaches,
there exists no theoretic expressivity study. In this work, we propose the
Fragment-WL test, an extension to the well-known Weisfeiler & Leman (WL) test,
which enables the theoretic analysis of these fragment-biased GNNs. Building on
the insights gained from the Fragment-WL test, we develop a new GNN
architecture and a fragmentation with infinite vocabulary that significantly
boosts expressiveness. We show the effectiveness of our model on synthetic and
real-world data where we outperform all GNNs on Peptides and have 12% lower
error than all GNNs on ZINC and 34% lower error than other fragment-biased
models. Furthermore, we show that our model exhibits superior generalization
capabilities compared to the latest transformer-based architectures,
positioning it as a robust solution for a range of molecular modeling tasks.

通过理论分析和扩展 Fragment-WL 测试，我们提出了一种基于片段信息的高阶图神经网络架构，大大增强了理论表达力，并在合成和真实数据中优于其它片段偏置模型，以及与最新的基于 Transformer 的架构相比表现出更强大的泛化能力，成为分子建模任务的可靠解决方案。