G-Signatures: Global Graph Propagation With Randomized Signatures

B Schäfl, L Gruber, J Brandstetter, S Hochreiter
[Johannes Kepler University Linz & Microsoft Research]

G-Signatures: 基于随机签名的全局图传播

要点:

  1. G-Signatures 是一种新的图学习方法，通过随机签名进行全局图传播；
  2. 引入图提升和潜空间路径映射的概念，将图结构化数据视为潜空间路径；
  3. G-Signatures 在提取全局图属性方面表现出色，内存和计算预算大幅减少；
  4. 实验表明，G-Signatures 在一些分类和回归任务中显示出优势。

一句话总结:
G-Signatures 是一种新的图学习方法，通过随机签名实现全局图传播，可以高效、可扩展地解决大型图问题。

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.

论文链接：https://arxiv.org/abs/2302.08811
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