Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures. However, GNNs suffer from over-smoothing node information and, therefore, struggle to solve tasks where global graph properties are relevant. We introduce G-Signatures, a novel graph learning method that enables global graph propagation via randomized signatures. G-Signatures use a new graph lifting concept to embed graph structured information, which can be interpreted as path in latent space. We further introduce the idea of latent space path mapping, which allows us to repetitively traverse latent space paths, and, thus globally process information. G-Signatures excel at extracting and processing global graph properties, and effectively scale to large graph problems. Empirically, we confirm the advantages of our G-Signatures at several classification and regression tasks.

翻译：图神经网络（GNN）已发展为最流行的深度学习架构之一。然而，GNN存在节点信息过度平滑的问题，因此难以处理与全局图属性相关的任务。我们提出G-签名，一种通过随机签名实现全局图传播的新型图学习方法。G-签名采用新的图提升概念来嵌入图结构信息，可理解为潜在空间中的路径。我们进一步引入潜在空间路径映射的思想，允许重复遍历潜在空间路径，从而实现全局信息处理。G-签名在提取和处理全局图属性方面表现出色，并能有效扩展至大规模图问题。通过多个分类与回归任务的实验，我们验证了G-签名的优势。