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：图形神经网络(GNNs)已经演变成最受欢迎的深层学习结构之一,然而,GNNs却受到过度移动的节点信息的影响,因此难以解决与全球图形属性有关的任务。我们引入了G-信号,这是一个新颖的图形学习方法,能够通过随机化的签名进行全球图形传播。G-信号使用一个新的图形提升概念来嵌入图形结构信息,可以被解释为隐蔽空间的路径。我们进一步引入了潜伏空间路径绘图的理念,这使我们能够重复穿越潜在空间路径,从而能够在全球处理信息。G-信号在提取和处理全球图形属性方面十分出色,并有效地将规模扩大到大图形问题。我们经常地确认我们G-信号在一些分类和回归任务上的优势。