论文标题
PWLR图表示：持续的Weisfeiler-Lehman方案，随机步行用于图形分类
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph Classification
论文作者
论文摘要
本文介绍了持续的Weisfeiler-Lehman随机步行方案（缩写为PWLR），用于图形表示，这是一个新型的数学框架，可产生具有离散和连续节点特征的图形的可解释的低维度表示的集合。提出的方案有效地结合了归一化的Weisfeiler-Lehman程序，在图表上随机行走以及持续的同源性。因此，我们整合了图形的三个不同属性，即局部拓扑特征，节点学位和全局拓扑不变，同时保留图形扰动的稳定性。这概括了Weisfeiler-Lehman过程的许多变体，这些变体主要用于嵌入具有离散节点标签的图形。经验结果表明，这些表示形式可以有效地用于与最先进的技术在用离散节点标签分类的图表中，并在对具有连续节点特征的人分类时增强性能。
This paper presents the Persistent Weisfeiler-Lehman Random walk scheme (abbreviated as PWLR) for graph representations, a novel mathematical framework which produces a collection of explainable low-dimensional representations of graphs with discrete and continuous node features. The proposed scheme effectively incorporates normalized Weisfeiler-Lehman procedure, random walks on graphs, and persistent homology. We thereby integrate three distinct properties of graphs, which are local topological features, node degrees, and global topological invariants, while preserving stability from graph perturbations. This generalizes many variants of Weisfeiler-Lehman procedures, which are primarily used to embed graphs with discrete node labels. Empirical results suggest that these representations can be efficiently utilized to produce comparable results to state-of-the-art techniques in classifying graphs with discrete node labels, and enhanced performances in classifying those with continuous node features.
