While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes. We present PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. Clarifying a point of contention in the literature, we show which step of PhUSION produces the different kinds of embeddings and what steps can be used by both. Moreover, by aggregating the PhUSION node embeddings, we obtain graph-level features that model information lost by previous graph feature learning and kernel methods. In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35 methods, we systematically reveal successful design choices for node and graph-level machine learning with embeddings.

中文翻译：

---

节点接近性是您所需要的：统一的结构和位置节点以及图形嵌入

尽管大多数网络嵌入技术都对网络中节点的相对位置进行建模，但近来人们对结构化建模（无论节点与任何特定节点的距离如何）建模感兴趣，这些模型对节点角色的等效性进行了建模。我们提出了PhUSION，这是一个用于计算结构和位置节点嵌入的基于邻近度的统一框架，该框架利用了完善的方法来计算节点邻近度分数。弄清文献中的一个争论点，我们展示了PhUSION的哪一步产生了不同种类的嵌入以及两者都可以使用什么步骤。此外，通过聚合PhUSION节点嵌入，我们获得了图级特征，该特征可以对以前的图特征学习和核方法丢失的信息进行建模。在包含10多个数据集，4个任务和35种方法的全面实证研究中，