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 conversion concept to embed graph structured information which can be interpreted as paths in latent space. We further introduce the idea of latent space path mapping. This allows us to iteratively 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 G-Signatures at several classification and regression tasks.

翻译：图神经网络（GNN）已发展成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，因此难以解决与全局图属性相关的任务。我们提出G-Signatures，一种通过随机签名实现全局图传播的新型图学习方法。G-Signatures利用新的图转换概念嵌入图结构信息，该信息可解释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，从而能够迭代遍历潜在空间路径，进而全局性处理信息。G-Signatures擅长提取和处理全局图属性，并能有效扩展到大规模图问题。实验结果表明，G-Signatures在多项分类和回归任务中具有显著优势。