发表机构
Pengcheng Laboratory (PCL); Beihang University; Beijing Academy of Blockchain and Edge Computing; Columbia University（鹏城实验室; 北京航空航天大学; 北京区块链与边缘计算研究院; 哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文证明无需训练，仅通过随机游走和匿名游走诱导的拓扑变换传播随机特征，即可生成捕获节点邻近性和结构角色的互补嵌入，在多种任务上性能媲美复杂模型且计算更高效。
AI 中文摘要
图表示学习主要集中于设计日益复杂的模型，以将图拓扑转换为向量表示（即嵌入）。然而，嵌入质量在多大程度上依赖于模型学习，而非底层的拓扑变换，仍不清楚。在此，我们表明，无需复杂的模型设计和基于梯度的训练，即可获得信息丰富的嵌入。通过随机游走和匿名游走所隐含的层次结构传播随机特征，可分别生成捕获节点邻近性和结构角色的嵌入。这两种免训练嵌入保留了图组织的互补方面，并在各种节点级、边级和图级任务中与经典及近期方法表现相当。它们通常需要显著更少的计算，从而实现了有利的质量-效率权衡。与单独使用任一嵌入类型相比，结合这两种嵌入可进一步提高某些任务的推理质量。我们的结果表明，信息丰富的图嵌入可以在任何学习操作应用之前，由精心选择的拓扑变换产生。
英文摘要
Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representations, or embeddings. However, the extent to which embedding quality depends on model learning, rather than on the underlying topological transformations, remains unclear. Here, we show that informative embeddings can be derived without complicated model design and gradient-based training. Propagating random features through implicit hierarchical structures induced by random walks and anonymous walks yields embeddings that capture node proximity and structural role, respectively. These two training-free embeddings preserve complementary aspects of graph organization and perform competitively with classic and recent methods across various node-, edge-, and graph-level tasks. They often require substantially less computation, resulting in a favorable quality-efficiency trade-off. Combining the two types of embeddings further improves inference quality of some tasks compared with using either embedding type alone. Our results suggest that informative graph embeddings can arise from carefully chosen topological transformations before any learning operation is applied.
