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.

本篇论文介绍了 PhUSION，这是一种用于计算结构和位置节点嵌入的基于接近度的统一框架，通过聚合节点嵌入，得到模拟先前的图特征学习和核方法丢失信息的图层次特征，主要适用于节点和图层次的机器学习。