The flexibility and effectiveness of message passing based graph neural networks (GNNs) induced considerable advances in deep learning on graph-structured data. In such approaches, GNNs recursively update node representations based on their neighbors and they gain expressivity through the use of node and edge attribute vectors. E.g., in computational tasks such as physics and chemistry usage of edge attributes such as relative position or distance proved to be essential. In this work, we address not what kind of attributes to use, but how to condition on this information to improve model performance. We consider three types of conditioning; weak, strong, and pure, which respectively relate to concatenation-based conditioning, gating, and transformations that are causally dependent on the attributes. This categorization provides a unifying viewpoint on different classes of GNNs, from separable convolutions to various forms of message passing networks. We provide an empirical study on the effect of conditioning methods in several tasks in computational chemistry.

翻译：基于消息传递的图神经网络（GNN）因其灵活性和有效性，推动了图结构数据深度学习领域的显著进展。在这类方法中，GNN基于邻居节点递归更新节点表示，并通过节点和边属性向量增强表达能力。例如，在物理和化学等计算任务中，使用相对位置或距离等边属性已被证明至关重要。本研究不探讨使用何种属性，而是关注如何基于这些信息进行条件化以提升模型性能。我们考虑三种条件类型：弱条件、强条件和纯条件，分别对应基于拼接的条件化、门控机制以及因果依赖于属性的变换。这一分类为不同类别的GNN提供了统一视角，涵盖可分离卷积与多种形式的消息传递网络。我们通过计算化学领域的多个任务对条件方法的效果进行了实证研究。